EP1787283A1 - Extraction of a melody on which an audio signal is based - Google Patents

Abstract

According to the invention, melody extraction or automatic transcription can be made significantly more stable and possibly also less expensive by sufficiently taking into account the assumption that the main melody is the portion of a piece of music which is perceived by humans as being the loudest and most prominent. Hence, in order to determine the melody of the audio signal, a melody line which extends across the time/spectral representation is determined (780) first by unambiguously assigning exactly one spectral component or a frequency bin of the time/spectral representation to each time slot or frame, i.e. according to a special embodiment of the invention, the spectral component that results in the sound having the maximum intensity on said frame.

Description

 Extraction of a melody underlying an audio signal

description

The present invention relates to the extraction of a melody underlying an audio signal. Such extraction may be used, for example, to obtain a transcribed representation of a melody underlying a monophonic or polyphonic audio signal, which may also be in an analog form or in a digital sampled form. Thus, melody extractions, for example, enable the generation of ringtones for mobile phones from any audio signal, e.g. Singing, humming, whistling or the like.

Already for some years now, the beeps of mobile phones have no longer been used solely to signal a call. Rather, they have become an entertainment factor with growing melodic capabilities of mobile devices and a status symbol among teenagers.

Earlier mobile phones offered in part the possibility to compose monophone ringtones on the device itself. However, this was complicated and often frustrating for musically under-educated users and unsatisfactory in their results. Therefore, this possibility or functionality has largely disappeared from newer phones.

In particular, modern phones that allow polyphonic signaling melodies or ringtones, offer such a wealth of combinations that an independent composition of a melody on such a mobile device is hardly possible. At most, ready-made melody and accompaniment patterns can be recombined to provide a limited amount of independent ringtones. Such combinability of ready-made melody and accompaniment patterns is implemented, for example, in the Sony-Ericsson T610 phone. In addition, however, the user relies on the purchase of commercially available pre-made ringtones.

It would be desirable to be able to provide the user with an intuitively operable interface for creating his own signaling melody, which does not require a large musical education, but is nevertheless suitable for the implementation of their own polyphonic melodies.

In most keyboards today, there is a so-called automatic accompaniment called functionality to automatically accompany a melody given the chords to be used. Quite apart from the fact that such keyboards provide no way to use an interface to a computer to transfer the tuned melody to a computer and there to convert into a suitable mobile phone format to use them as ringtones in a mobile phone can , the use of a keyboard to generate its own polyphonic signaling melodies for mobile phones for most users, because they are not able to operate this musical instrument.

In DE 102004010878.1 entitled "Apparatus and Method for Delivering a Signaling Tune" whose assignee is the same as the assignee of the present application and which was filed with the German Patent and Trademark Office on March 5, 2004, a method is described which uses a Java applet and server software to generate monophonic and polyphonic ringtones and send them to a mobile device, but the suggested procedures for extracting the melody from audio signals are very prone to error or have limited use proposed to thereby arrive at a melody of an audio signal that _ _

characteristic features are extracted from the audio signal to compare the same with corresponding features of pre-stored tunes, and then select as the generated tune that among the pre-stored ones giving the best match. However, this approach inherently limits melody recognition to the pre-stored set of tunes.

DE 102004033867.1 entitled "Method and apparatus for the rhythmic processing of audio signals" and DE 102004033829.9 entitled "Method and apparatus for generating a polyphonic melody", which were deposited on the same day, the German Patent and Trademark Office, employ But they also do not go into the actual melody recognition, but rather to the subsequent process of deriving an accompaniment from the melody together with a rhythmic and harmonic-dependent treatment of the melody.

For example, Bello, JP, Towards the Automated Analysis of Simple Polyphonic Music, deals with possibilities of melody recognition. A knowledge-based approach, University of London, Diss., January 2003, describes various types of start-time detection of notes, either at the local level Energy in time signal or based on an analysis in the frequency domain. In addition, various methods for melody line detection are described. The common feature of these approaches is that they are complicated in that the final melody is obtained indirectly by first processing or tracking several trajectories in the time / spectral representation of the audio signal, and finally, under these trajectories Selection of the melody line or the melody is made.

Also in Martin, KD, A Blackboard System for Automatic Transcription of Simple Polyphonic Music, MIT Media _ _

Laboratory Perceptual Computing Section 385, 1996, a possibility for automatic transcription is described, which is also based on the evaluation of several harmonic tracks in a time / frequency representation of the audio signal or the spectrogram of the audio signal.

Klapuri, AP: Signal Processing Methods for the Automatic Transcription of Music, Tampere University of Technology, Summary Diss., December 2003, and Klapuri, AP, Signal Processing Methods for the Automatic Transcription of Music, Tampere University of Technology, Diss. In December 2003, AP Klapuri, "Number Theoretical Means of Resolving a Mixture of Several Harmony Sounds." In Proceedings European Signal Processing Conference, Rhodes, Greece, 1998, AP Klapuri, "Sound Onset Detection by Applying Psychoacoustic Knowledge," in Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, Ariz., 1999, AP Klapuri, "Multipitch Estimation and Sound Separation by the Spectral Smoothness Principle," in Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, Utah, 2001, Klapuri AP and Astola JT, "Efficient Calculation of a Physiologically-motivated Representation for Sound", in P 14th IEEE International Conference on Digital Signal Processing, Santorini, Greece, 2002, AP Klapuri, "Multiple Fundamental Frequency Estimation based on Harmony and Spectral Smoothness", IEEE Trans. Speech and Audio Proc, 11 (6), pp. 804-816 , 2003, Klapuri AP, Eronen AJ and Astola JT, "Automatic Estimation of the Meter of Acoustic Musical Signals," Tempere University of Technology, Institute of Signal Processing, Report 1-2004, Tampere, Finland, 2004, ISSN: 1459-4595, ISBN: 952-15-1149 -4, various methods are described around the automatic transcription of music.

In the context of basic research on the subject extraction of a main melody as a special case of Polyphonic transcription is also Baumann, U .: A method for the detection and separation of multiple acoustic objects, Diss., Chair of human-machine communication, Technical University of Munich, 1995, highlight.

The above-mentioned different approaches to melody recognition or automatic transcription usually make special demands on the input signal. For example, they only allow piano music or only a certain number of instruments or exclude percussive instruments or the like.

The most viable approach to contemporary modern and popular music is the approach of Goto, as described, for example, in Goto, M.: A Robust Predominant-FO Estimation Method for Real-time Detection of Melody and Bass Lines in CD Recordings, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, pp.II-757-760, June 2000. The aim of this method is to extract a dominant melody and bass line, whereby the detour to the line finding again takes place via the selection under a plurality of trajectories, namely using so-called "agents." The method is thus complicated.

With the melody detection also Paiva R. P. u. a. : A Methodology for Detection of Melody in Polyphonic Musical Signals, 116th AES Convention, Berlin, May 2004. There, too, it is proposed to follow the path of trajectory tracking in the time / spectral representation. The document also deals with the segmentation of the individual trajectories, until they are processed into a sequence of notes.

It would be desirable to have a method of melody extraction or automatic transcription that is more robust and reliable for a wider variety of different audio signals. Such a robust System could provide a high time and cost savings in "query by huming" systems, ie in systems where a user is able to find songs by pre-sums in a database, as an automatic transcription for the system database reference files A robust transcription could, of course, also be used as a recording fron d and it would also be possible to use automatic transcription as a supplement to an audio ID system, that is, a system that recognizes audio files on a fingerprint contained in them because if not recognized by the audio ID system, such as due to a missing fingerprint, automatic transcription could alternatively be used to evaluate an incoming audio file.

Stably functioning automatic transcription would also allow for the production of similarity relationships associated with other musical features, e.g. Key, harmony and rhythm, such as for a "recomandation engine" or "suggestion engine". In musicology, a stable automatic transcription could create new views and lead to a re-examination of judgments on older music. Also, to preserve copyright by objectively comparing pieces of music, automatic transcription that is stable in use could be used.

In summary, the application of melody recognition or auto-transcription is not limited to the generation of ringtones for mobile phones mentioned above, but can generally serve as a support for musicians and those interested in music.

Therefore, the object of the present invention is to provide a more stable melody recognition scheme which works correctly for a wider variety of audio signals. - -

This object is achieved by a device according to claim 1 and a method according to claim 33.

The knowledge of the present invention is that the melody extraction or automatic transcription can be made much more stable and possibly even less expensive, if the assumption is sufficiently taken into account that the main melody is that portion of a piece of music that the person perceives loudest and most concise. In accordance with the present invention, in determining the melody of the audio signal, a melody line extending through the time / spectral representation is first of all determined by virtue of the fact that each time segment or frame - in a clear manner - has exactly one spectral component or a frequency bin is assigned to the time / spectral representation, namely, according to a specific embodiment, that which leads to the sound result with the maximum intensity at that frame.

According to a preferred embodiment of the present invention, the above musicological statement that the main melody is that portion of a piece of music that the person perceives most loudly and succinctly is taken into account in two ways. Namely, according to this embodiment, the time / spectral representation or spectrogram of an audio signal of interest is scaled using the equal volume curves reflecting human volume perception to determine the melody of the audio signal based on the resulting perceptual time / spectral representation , More specifically, according to this embodiment, the spectrogram of the audio signal is first logarithmized, so that the logarithmized spectral values indicate the sound pressure level. Subsequently, the logarithmized spectral values of the logarithmic _ _

Spectrographs are mapped to perceptual spectral values, depending on their respective value and the spectral component to which they belong. Functions are used that represent the curves of equal volume as sound pressure as a function of spectral components or as a function of the frequency and are assigned to different volumes. Again, the perceptual spectrum is delogarithmized to produce a time / sound spectrum from the result by forming sums of delogarithmized perceptual spectral values per frame for predetermined spectral components. These sums comprise the delogarithmized perceptual spectral value at the respective spectral component as well as the delogarithmized perceptual spectral values at the spectral components which form an overtone to the respective spectral component. The time / sound spectrum thus obtained represents a version of the time / spectral representation derived therefrom.

Preferred embodiments of the present invention will be explained in more detail below with reference to the accompanying drawings. Show it:

Fig. 1 is a block diagram of an apparatus for generating a polyphonic melody;

FIG. 2 is a flow chart illustrating the operation of the extractor of the apparatus of FIG. 1; FIG.

Fig. 3 is a more detailed flow chart illustrating the operation of the extractor of the apparatus of Fig. 1 in the case of a polyphonic audio input signal;

4 shows an exemplary example of a time / spectral representation or a spectrogram of a - -

Audio signal, as might occur in the frequency analysis in Fig. 3;

Fig. 5 is a logarithmic spectrogram, as shown by the logarithm of Fig. 3;

Fig. 6 is a diagram with the curves of equal volume, as the evaluation of the spectrum in Fig. 3 to

Basically lie;

Fig. 7 is a graph of an audio signal used before the actual logarithm in Fig. 3 to obtain a reference value for logarithmization;

FIG. 8 shows a perceptual spectrogram as obtained after the evaluation of the spectrogram of FIG. 5 in FIG.

3 is obtained;

FIG. 9 is the melody line resulting from the perceptual spectrum of FIG. 8 through the melody line determination of FIG. 3, plotted in the time / spectral domain; FIG.

FIG. 10 is a flow chart illustrating the general segmentation of FIG. 3; FIG.

Fig. 11 is a schematic representation of an exemplary

Melody line progression in the time / spectral domain;

FIG. 12 is a schematic representation of a section of the melody line progression diagram of FIG. 11, for illustrating the mode of operation of the filtering in the general segmentation of FIG. 10; FIG.

Fig. 13 is the melody line progression of Fig. 9 after the frequency domain confinement in the general segmentation of Fig. 10; - -

Fig. 14 is a schematic drawing, in which a section of a melody line is shown, for

Illustrate the operation of the penultimate step in the general segmentation of FIG.

10;

Fig. 15 is a schematic drawing of a section of a melody line to illustrate the mode of operation of the segmentation in the general

Segmentation of Fig. 10;

FIG. 16 is a flowchart for illustrating the

Gap closure in Fig. 3;

Fig. 17 is a schematic drawing for illustrating the procedure for setting the variable

Halftone vector in Fig. 3;

Fig. 18 is a schematic drawing for illustrating the gap closing of Fig. 16;

FIG. 19 is a flow chart illustrating the harmony mapping in FIG. 3; FIG.

FIG. 20 shows a schematic representation of a detail from the melody line progression for illustrating the mode of action of the harmoniemappings according to FIG. 19; FIG.

FIG. 21 is a flowchart illustrating the vibration detection and the vibrator compensation in FIG. 3; FIG.

FIG. 22 shows a schematic representation of a segment profile for illustrating the procedure according to FIG. 21; FIG. - -

FIG. 23 shows a schematic illustration of a detail from the melody line progression in order to illustrate the procedure for the statistical correction in FIG. 3; FIG.

FIG. 24 is a flow chart illustrating the procedure of onset detection and correction in FIG. 3; FIG.

Fig. 25 is a graph showing an exemplary filter transfer function for use in onset detection of Fig. 24;

FIG. 26 is a schematic diagram of a two-way rectified filtered audio signal and the envelope thereof as used for onset detection and correction in FIG. 24; FIG.

FIG. 27 is a flow chart illustrating the operation of the extractor of FIG. 1 in the case of monophonic audio input signals; FIG.

Fig. 28 is a flowchart illustrating sound separation in Fig. 27;

29 is a schematic representation of a section of the amplitude curve of the spectrogram of a

Audio signal along a segment to illustrate the operation of the

Sound separation according to Fig. 28;

FIGS. 30 a and b are schematic representations of a section of the amplitude profile of the spectrogram of an audio signal along a segment for the purpose of FIG

Illustrating the operation of the

Sound separation according to Fig. 28; - -

Fig. 31 is a flowchart illustrating the tone smoothing in Fig. 27;

Fig. 32 is a schematic representation of a segment of the melody line course to illustrate the

Tone smoothing procedure of Fig. 31;

33 is a flow chart illustrating the offset

Recognition and correction in Fig. 27;

Fig. 34 is a schematic representation of a section of a two-way rectified filtered

Audio signal and its interpolation to

Illustrating the procedure of Fig. 33; and

FIG. 35 shows a detail of a two-way rectified filtered audio signal and its interpolation in the case of a potential segment extension.

With reference to the following description of the figures, it is pointed out that the present invention is described there merely by way of example with reference to a specific application, namely the generation of a polyphonic ringing melody from an audio signal. However, it should be pointed out explicitly that the present invention is of course not limited to this application, but that a melody extraction or automatic transcription according to the invention can also be used elsewhere, for example to facilitate searching in a database, the mere recognition of Pieces of music, enabling copyright protection by objectively comparing pieces of music or the like, or even merely transcribing audio signals in order to be able to display the transcription result to a musician. - -

Fig. 1 shows an embodiment of a device for generating a polyphonic melody from an audio signal containing a desired tune. In other words, FIG. 1 _{v shows} a device for the rhythmic and harmonic conditioning and re-instrumenting of a melody-representing audio signal and for supplementing the resulting melody with a suitable accompaniment.

The apparatus of FIG. 1, indicated generally at 300, includes an input 302 for receiving the audio signal. In the present case, it is assumed by way of example that the device 300 or the input 302 expects the audio signal in a time-sample representation, for example as a WAV file. However, the audio signal could also be present in other form at input 302, such as in uncompressed or compressed form or in a frequency band representation. The device 300 further comprises an output 304 for outputting a polyphonic melody in any format, in the present case starting from an output of the polyphonic melody in MIDI format (MIDI = musical instrument digital interface). Between the input 302 and the output 304, an extraction device 304, a rhythm device 306, a key device 308, a harmony device 310 and a synthesis device 312 are connected in series in this order. Furthermore, the device 300 comprises a melody memory 314. An output of the key device 308 is connected not only to an input of the subsequent harmony device 310, but also to an input of the melody memory 314. Accordingly, the input of the harmony device 310 is not limited to the output of the processing direction but also with an output of the melody memory 314. Another input of the melody memory 314 is provided to receive a provision identification number ID. Another input of the synthesizer 312 is configured to receive style information. The meaning - -

The style information and the provision identification number are shown in the following functional description. Extractor 304 and rhythm means 306 together form a rhythm editor 316.

Having described the structure of the apparatus 300 of Fig. 1 above, its operation will be described below.

The extraction device 304 is designed to subject the audio signal received at the input 302 to note extraction or recognition in order to obtain a note sequence from the audio signal. The note sequence 318, which forwards the extraction device 304 to the rhythm device 306, in the present exemplary embodiment is in a form in which, for each note n, a note start time t _n indicating the beginning of the note, for example, in seconds, a tone or note duration τ _n , which indicates the note duration of the note, for example, in seconds, a quantized note or pitch, ie C, Fis or the like, for example as a MIDI note, a volume Ln of the note and an exact frequency f _{n of} the tone or the note is contained in the note sequence, where n is to represent an index for the respective note in the note sequence, which increases with the order of the successive notes or indicates the position of the respective note in the note sequence.

The melody recognition or autotranscription, which by the

Next, means 304 for generating the note sequence 318 will be described later with reference to FIGS. 2-35.

The note sequence 318 still represents the melody as it was also represented by the audio signal 302. The note sequence 318 is now fed to the rhythm device 306. The rhythm device 306 is designed to - -

analyze the supplied note sequence to one measure length, one start, i. a clock raster to determine the note sequence and thereby the individual notes of the note sequence of appropriate clock-quantified lengths, such as. whole, half, quarter, eighth notes, etc., for the particular bar, and to match the notes' notes to the bar pattern. The note sequence that the rhythm device 306 outputs, thus represents a rhythmically processed note sequence 324.

At the rhythmically processed note sequence 324, the key device 308 performs a key determination and possibly a key correction. More specifically, the means 308 determines, based on the note sequence 324, a major key of the user melody represented by the note sequence 324 and the audio signal 302, inclusive of the pitch gender, i. Major or minor, for example the sung piece. Thereafter, it also recognizes, at this point, non-sounding notes in the note sequence 114 and corrects them to arrive at a harmonic-sounding final result, a rhythmically edited and pitch-corrected note sequence 700 forwarded to the harmony 310 and a key ¬ corrected form represents the desired by the user melody.

The mode of operation of the device 324 with regard to the key determination can be carried out in various ways. For example, the key determination may refer to those described in the article Krumhansl, Carol L.: Cognitive Foundations of Musical Pitch, Oxford University Press, 1990, or in the article Temperley, David: The Cognition of basic musical structures. The MIT Press, 2001, described manner.

The harmony device 310 is configured to receive the note sequence 700 from the device 308 and to the tune represented by this note sequence 700 - -

will find a suitable accompaniment. For this purpose, device 310 acts or acts in a cyclic manner. Specifically, the means 310 operates on each clock as determined by the clock raster set by the rhythm means 306, such that it provides statistics about the tones of the notes T _n occurring in the respective clock. The statistics of the occurring tones are then compared with the possible chords of the major scale scale as determined by the key device 308. Means 310 then selects, among the possible chords, in particular, that chord whose tones best match the notes that are in the respective measure, as indicated by statistics. In this way, the means 310 determines, for each clock, the chord which best fits the notes or notes, for example, in the respective clock. In other words, the means 310 allocates chord levels of the root key to the pitches found by the means 306 in dependence on the pitch, so that a chord progression forms over the course of the melody. Consequently, at the output of the device 310, in addition to the rhythmically processed and key-corrected note sequence including NL, it also outputs a chord step specification to the synthesis device 312 for each measure.

Synthesizer 312 uses style information that can be entered by a user as indicated by case 702 to perform the synthesis, ie, artificially generate the eventually resulting polyphonic melody. For example, the stylist information allows a user to select from four different styles in which the polyphonic melody can be generated, namely Pop, Techno, Latin or Reggae. For each of these styles, either one or more companion patterns are stored in the synthesis device 312. To generate the accompaniment, the synthesizer 312 now uses the accompaniment pattern (s) indicated by the style information 702. to - -

To produce the accompaniment, the synthesizer 312 appends the accompaniment patterns per clock. If the chord determined by the device 310 is a clock around the chord version in which an accompaniment pattern already exists, then the synthesizer 312 simply selects the corresponding accompaniment pattern for the current style for that accompaniment clock. If, however, the chord determined by means 310 is not the one in which an accompaniment pattern is stored in means 312 for a particular clock, synthesizer 312 shifts the notes of the accompaniment pattern by the corresponding semitone number and changes the third Sext and fifth a semitone in the case of another tone gender, namely by shifting one semitone up in the case of a major chord reversed in the case of a minor chord.

Further, the synthesizer 312 orchestrates the melody represented by the note string 700 forwarded from the harmony 310 to the synthesizer 312 to obtain a main melody, and then combines accompaniment and main melody into a polyphonic melody, exemplified herein in the form of MIDI File at output 304 outputs.

The key device 308 is further configured to store the note string 700 in the melody memory 314 under a provision identification number. If the user is dissatisfied with the outcome of the polyphonic tune at exit 304, he may reenter the provisioning identification number along with a new style information in the apparatus of Figure 1, whereupon the melody store 314 forwards the sequence 700 stored under the staging identification number to the harmony facility 310, which then determines the chords as described above, whereupon the synthesizer 312 uses the new style information depending on the chords a new one Accompaniment and depending on the note sequence 700 creates a new main melody and joins together to form a new polyphonic melody at the output 304.

The functioning of the extraction device 304 will now be described below with reference to FIGS. 2-35. Referring first to FIGS. 2--26, the procedure for melody recognition in the case of polyphonic audio signals 302 at the input of device 304 will be described.

Fig. 2 shows first the rough procedure in the melody extraction or autotranscription. Starting point is the reading or the input of the audio file in a step 750, which, as described above, can be present as a WAV file. The device 304 then performs a frequency analysis on the audio file in a step 752 to thereby provide a time / frequency representation or spectrogram of the audio signal contained in the file. In particular, step 752 includes decomposing the audio signal into frequency bands. In the course of a windowing, the audio signal is subdivided into preferably time-overlapping time segments which are then spectrally decomposed in each case in order to obtain a spectral value for each of a set of spectral components for each time interval or each frame. The set of spectral components depends on the choice of the transformation underlying the frequency analysis 752, a particular embodiment of which will be explained below with reference to FIG. 4.

After step 752, means 304 determines a weighted amplitude spectrum or perceptual spectrogram in step 754. The detailed procedure for determining the perceptual spectrogram will now be described in greater detail with reference to FIGS. 3-8. The result of step 754 is a rescale of the spectrogram obtained from the frequency analysis 752 below - -

Using the curves of equal volume, which reflect the human perceptual perception to adapt the spectrogram to the human perception.

The processing 756 subsequent to step 754 uses, among other things, the perceptual spectrogram obtained from step 754 to finally obtain the melody of the output signal in the form of a melody line articulated in note segments, i. in a form in which groups of consecutive frames are each assigned the same pitch with each other, these groups being spaced apart in time over one or more frames, thus not overlapping and thus corresponding to note segments of a monophonic melody.

In Fig. 2, the processing 756 is decomposed into three substeps 758, 760 and 762. In the first sub-step, the perceptual spectrogram is used to obtain a time / fundamental frequency representation from the same, and in turn to use this time / fundamental frequency representation to determine a melody line such that each frame in a unique manner has exactly one spectral component Frequency bin is assigned. The time / fundamental representation accounts for the division of sounds into partial tones by first delogarithmizing the perceptual spectrogram from step 754 to summate for each frame and for each frequency bin the delogarithmized perceptual spectral values at that frequency bin and the overtones to the respective frequency bin. The result is a sound spectrum per frame. From this sound spectrum, the determination of the melody line is carried out by selecting for each frame the fundamental tone or the frequency or that frequency bin at which the sound spectrum has its maximum. The result of step 758 is thus virtually a melody line function that everyone Frame assigns exactly one frequency bin. This melody line function in turn defines a melody line progression in the time / frequency domain or a two-dimensional melody matrix spanned by the possible speech components or bins on one side and the possible frames on the other side.

The following substeps 760 and 762 are provided to segment the continuous melody line, thus giving single notes. In Fig. 2, the segmentation is divided into two substeps 760 and 762, depending on whether the segmentation is at input frequency resolution, i. in frequency bin resolution, or whether the segmentation is in halftone resolution, i. after quantizing the frequencies to semitone frequencies.

The result of processing 756 is processed in step 764 to generate a sequence of notes from the melody line segments, each note being assigned a note start time, a note duration, a quantized pitch, an exact pitch, and so on.

Having described the operation of the extractor 304 of FIG. 1 in more general terms above with reference to FIG. 2, the operation of the same will be described in more detail in the following with reference to FIG 302 music is of polyphonic origin. The distinction between polyphonic and monophonic audio signals stems from the observation that monophonic audio signals often originate from less well-trained individuals and therefore have musical inadequacies that require a slightly different approach to segmentation.

In the first two steps 750 and 752, FIG. 3 agrees with FIG. 2, ie an audio signal is initially provided 750 and this then a frequency analysis 752 subjected. For example, according to an embodiment of the present invention, the WAV file is in a format since the individual audio samples are sampled at a sampling frequency of 16 kHz. The individual samples are present, for example, in a 16-bit format. Furthermore, it is assumed in the following by way of example that the audio signal is present as a mono-file.

The frequency analysis 752 can then be performed, for example, by means of a warped filter bank and an FFT (Fast Fourier

Transformation) are performed. In particular, in frequency analysis 752, the sequence of audio values is first windowed with a window length of 512 samples, operating on a hopsize of 128 samples, i. the windowing is repeated every 128 samples. Together with the sampling rate of 16 kHz and the quantization resolution of 16 bits, these parameters represent a good compromise between time and frequency resolution. In these exemplary settings, a time frame corresponds to a duration of 8 milliseconds.

The warped filter bank is used according to a special embodiment for the frequency range up to about 1550 Hz. This is necessary to achieve a sufficiently good resolution for low frequencies. For a good halftone resolution enough frequency bands should be available. At a lambda value of -0.85 at 16 kHz sampling rate, approximately two to four frequency bands correspond to one semitone on a frequency of 100 Hz. For low frequencies, each frequency band can be assigned a semitone. For the frequency range up to 8 kHz the FFT is used. The frequency resolution of the FFT is sufficient from about 1,550 Hz for a good halftone representation. Here approx. Two to six frequency bands correspond to one semitone.

In the implementation described above by way of example, the transient response of the warped filter bank must be taken into account. Preferably, therefore, a temporal synchronization at the combination of the two transformations. For example, the first 16 frames of the filterbank output are discarded, as well as the last 16 frames of the output spectrum FFT are disregarded. With a suitable design, the amplitude level of the filter bank and FFT is identical and requires no adaptation.

FIG. 4 shows by way of example an amplitude spectrum or a time / frequency representation or a spectrogram of an audio signal, as obtained by the preceding exemplary embodiment of a combination of a warped filter bank and an FFT. Along the horizontal axis in FIG. 4, the time t is plotted in seconds s, while along the vertical axis the frequency f is in Hz. The height of the individual spectral values is gray scale. In other words, the time / frequency representation of an audio signal is a two-dimensional field spanned by the possible frequency bins or spectral components on one side (vertical axis) and the time segments or frames on the other side (horizontal axis), each Position of this field is assigned to a specific tuple of frame and Frequenzbin a spectral value or an amplitude.

According to a specific embodiment, the amplitudes in the spectrum of Fig. 4 are still post-processed in the frequency analysis 752, since the amplitudes calculated by the warped filterbank may sometimes not be accurate enough for subsequent processing. The frequencies that are not exactly at the center frequency of a frequency band, have a lower amplitude value than frequencies that correspond exactly to the center frequency of a frequency band. In addition, in the output spectrum of the warped filter bank, a crosstalk arises on adjacent frequency bands, which are also referred to as bins or frequency bins. - -

To correct the erroneous amplitudes, the effect of crosstalk can be exploited. This error affects a maximum of two adjacent frequency bands in each direction. According to one embodiment, therefore, in the spectrogram of Fig. 4, within each frame, the amplitudes of adjacent bins are added to the amplitude value of a middle bin, and this for all bins. Because there is a risk that incorrect amplitude values will be calculated when two audio frequencies are particularly close together in a music signal, and thus phantom frequencies are generated having greater values than the two original sine parts, according to a preferred embodiment only the amplitude values of the directly adjacent neighbor bins will be generated added to the amplitude of the original signal component. This represents a compromise between accuracy and the occurrence of side effects caused by the addition of the directly adjacent bins. Despite the lower accuracy of the amplitude values, this trade-off is acceptable in the context of melody extraction, since the change in the calculated amplitude value can be neglected in the addition of three or five frequency bands. In contrast, the emergence of phantom frequencies is much more important. The generation of phantom frequencies increases with the number of simultaneous sounds in a piece of music. When searching for the melody line, this can lead to wrong results. The calculation of the exact amplitudes is preferably carried out both for the warped filter bank and for the FFT, so that the music signal is subsequently represented over the entire frequency spectrum by an amplitude level.

The above embodiment for a signal analysis from a combination of a warped filter bank and an FFT enables a hearing-friendly frequency resolution and the presence of sufficient frequency bins per semitone. For more details on implementation, refer to the thesis of Claas Derboven entitled "Implementation and Management Investigation of a method for the detection of sound objects from polyphonic audio signals ", developed at the Technical University of Ilmenau in 2003, and the diploma thesis of Olaf Schleusing entitled" Investigation of frequency domain transformations for metadata extraction from audio signals ", developed at the Technical University Ilmenau in 2002 , referenced.

As mentioned above, the analysis result of frequency analysis 752 is a matrix of spectral values. These spectral values represent the volume by the amplitude. However, the human volume perception possesses a logarithmic division. It thus makes sense to adapt the amplitude spectrum to this classification. This is done in a logarithmization 770 following step 752. In logarithmization 770, all spectral values are logarithmized to the level of the sound pressure level, which corresponds to the logarithmic perception of loudness of humans. More specifically, in the logarithmization 770 to the spectral value p in the spectrogram obtained from the frequency analysis 752, p is mapped to a sound pressure level value and a logarithmic spectral value L, respectively

L [dB] = 20 Log (^)

po

Here Po indicates the reference sound pressure, i. the volume level that has the smallest perceptible sound pressure at 1000 Hz.

Within logarithmization 770, this reference value must first be determined. While in the analog signal analysis as

Reference value of the smallest perceptible sound pressure p _{0 is} used, this lawfulness can be applied to the digital - -

Signal processing not readily transmitted. In order to determine the reference value, according to one exemplary embodiment, therefore, a trial audio signal is used for this purpose, as illustrated in FIG. 7. FIG. 7 shows the sample audio signal 772 over the time t, the amplitude A being plotted in the Y direction in the smallest representable digital units. As can be seen, the sample audio signal or reference signal 772 is present with an amplitude value of one LSB or with the smallest representable digital value. In other words, the amplitude of the reference signal 772 only oscillates by one bit. The frequency of the reference signal 772 corresponds to the frequency of the highest sensitivity of the human auditory threshold. However, other determinations of the benchmark may be more beneficial on a case-by-case basis.

In FIG. 5, the result of the logarithmization 770 of the spectrogram from FIG. 4 is shown by way of example. If, due to the logarithmization, a part of the logarithmic spectrogram is in the negative value range, these negative spectral or amplitude values are set to 0 dB to avoid non-meaningful results in the further processing in order to obtain positive results over the entire frequency range , For the sake of brevity, it should be noted that in FIG. 5 the logarithmic spectral values are shown in the same manner as in FIG. 4, i. arranged in a spanned by the time t and the frequency f matrix and grayscale depending on the value, namely the darker the greater the respective spectral value.

The volume rating of humans is frequency dependent. Therefore, the logarithmic spectrum, as it results from the logarithm 770, in a subsequent step - -

772 in order to adapt to this frequency-dependent assessment of the human. For this purpose, the curves of equal volume 774 are used. In particular, the score 772 is therefore necessary to match the different amplitude scores of the musical sounds across the frequency scale of human perception since, according to human perception, the amplitude values of low frequencies experience a lower score than amplitudes of higher frequencies.

For the curves 774 of the same volume, the curve characteristic from DIN 45630 Part 2, German Institute for Standardization e.V., Fundamentals of Sound Measurement, Normal Curves of the Same Volume, 1967, was used here by way of example. The graph is shown in FIG. 6. As can be seen in FIG. 6, the equal volume curves 774 are respectively associated with different volume levels indicated in phon. In particular, these curves 774 represent functions that assign each frequency a sound pressure level in dB such that all the sound pressure levels that are on the respective curve correspond to the same volume level of the respective curve.

Preferably, the equal volume curves 774 are present in the device 204 in analytic form, it being of course also possible to provide a look-up table corresponding to each pair of frequency bins and

Sound level quantization value assigns a volume level value. For the volume curve with the lowest volume level, for example, the formula

(2) be used. However, deviations in the low- and high-frequency value range are present between this curve and the hearing threshold under DIN standard. For adaptation, the function parameters of the resting hearing threshold can be changed according to the above equation to correspond to the curve of the lowest loudness curve of the abovementioned DIN standard of FIG. 6. Thereafter, this curve is shifted vertically in the direction of higher volume levels at intervals of 10 dB, and the function parameters are adapted to the respective characteristic of the function graphs 774. The intermediate values are determined in 1 dB increments by linear interpolation. Preferably, the function with the highest value range can evaluate a level of 100 dB. This is sufficient, since a word width of 16 bits corresponds to a dynamic range of 98 dB.

Based on the equal volume curves 774, in step 772 means 304 forms each logarithmic spectral value, i. 5, depending on the frequency f or the frequency bin to which it belongs, and its value, which represents the sound pressure level, on a perceptual spectral value representing the volume level.

The result of this procedure for the case of the logarithmic spectrum of FIG. 5 is shown in FIG. As can be seen, in the spectrogram of Fig. 8, low frequencies are no longer of great importance. Higher frequencies and their overtones are emphasized by this rating. This also corresponds to human perception for evaluating the volume for different frequencies. - -

The above-described steps 770-774 illustrate possible substeps of step 754 of FIG. 2.

The method of FIG. 3 proceeds to evaluation 772 of the spectrum in a step 776 with a fundamental frequency determination or with the calculation of the total intensity of each sound in the audio signal. For this purpose, in step 776 the intensities of each fundamental tone and the associated harmonics are added up. From a physical point of view, a sound consists of a fundamental tone under the corresponding partial tones. The partial tones are integer multiples of the fundamental frequency of a sound. The partial or overtones are also called harmonics. In order to sum up the intensity of each fundamental tone and its respective harmonics, a harmonic grid 778 is used in step 776 in order to search for each possible fundamental tone, i. each frequency bin to search for overtones or overtones that are an integer multiple of the respective fundamental tone. Thus, at a particular frequency bin as a fundamental, further frequency bins corresponding to an integer multiple of the frequency bin of the fundamental are assigned as harmonic frequencies.

In step 776, the intensities in the spectrogram of the audio signal at the respective fundamental tone and its harmonics are then added up for all possible fundamental tone frequencies. In this case, however, a weighting of the individual intensity values is carried out, because due to several sounds occurring simultaneously in a piece of music, there is the possibility that the fundamental tone of a sound from an upper tone of another sound with a - -

low-frequency fundamental tone is obscured. Also overtones of a sound can be obscured by overtones of another sound.

Nevertheless, to determine the associated tones of a sound, a tone model based on the principle of the Mosataka Goto model and adapted to the spectral resolution of the frequency analysis 752 is used in step 776, the tone model of Goto in Goto, M.: A Robust Predominant-FO Estimation Method for Real-time Detection of Melody and Bass Lines, in CD Recordings, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Istanbul, Turkey, 2000.

Starting from the possible fundamental frequency of a sound, harmonic grid 778 assigns the respective harmonic frequencies for each frequency band or frequency bin. According to a preferred embodiment, overtones are searched for fundamental frequencies in only one particular frequency bin range, such as from 80 Hz to 4,100 Hz, and harmonics are considered only up to the 15th order. The overtones of different sounds can be assigned to the sound model of several fundamental frequencies. By this effect, the amplitude ratio of a sought sound can be changed considerably. To attenuate this effect, the amplitudes of the partial tones are evaluated with a halved Gaussian filter. The keynote receives the highest value. All the following partial tones receive a lower weighting according to their order, the weighting decreasing in a Gauss-shaped manner, for example, with increasing order. Thus, an overtone amplitude of another sound, which obscures the actual overtone, has no particular JU

Effect on the overall result of a searched vote. As the frequency resolution of the spectrum becomes lower for higher frequencies, a bin with the corresponding frequency does not exist for each higher order overtone. Due to the crosstalk to the adjacent bins of the frequency environment of the searched overtone, the amplitude of the searched overtone can be simulated relatively well by means of a Gaussian filter over the nearest frequency bands. Therefore, overtone frequencies or the intensities thereon do not have to be determined in units of frequency bins, but interpolation can also be used to accurately determine the intensity value at the overtone frequency.

However, the summation over the intensity values is not performed directly on the perceptual spectrum from step 772. Rather, first in step 776, the perceptual spectrum of FIG. 8 is first delogarithmized with the aid of the reference value from step 770. The result is a delogarithmized perceptual spectrum, ie an array of delogarithmized perceptual spectral values for each frequency bin and frame tuple. Within this delogarithmized perceptual spectrum, for each possible root, the spectral value of the fundamental and the possibly interpolated spectral values are added together using the harmonic grid 778 of the associated harmonic, giving a sound intensity value for the frequency range of all possible fundamental frequencies, for each frame - in the previous example only within the range of 80 to 4000 Hz. In other words, the result of step 776 is a sound spectrogram, step 776 itself being a sound spectrogram - -

Level addition within the spectrogram of the audio signal corresponds. For example, the result of step 776 is entered into a new matrix having one row for each frequency bin within the frequency range of possible fundamental frequencies and one column for each frame, with each matrix element, i. at each intersection of column and row, the result of the summation for the corresponding frequency bin is entered as the fundamental tone.

Next, in a step 780, a preliminary determination of a potential melody line is made. The melody line corresponds to a function over time, namely a function that uniquely assigns to each frame exactly one frequency band or frequency bin. In other words, the melody line determined in step 780 defines a track along the domain of definition of the sound spectrogram or matrix of step 776, the track never being ambiguous along the frequency axis.

The determination is made in step 780 such that the maximum amplitude is determined for each frame over the entire frequency range of the sound spectrogram, i. the largest summation value. The result, i. the melody line largely corresponds to the basic course of the melody of the audio track underlying the audio signal 302.

The evaluation of the spectrogram with the equal volume curves in step 772 and the search for the

Sound result with the maximum intensity in step 780 carry the musicological statement into account that the -

The main melody is the part of a song that man perceives loudest and most concisely.

The above-described steps 776-780 represent possible substeps of step 758 of FIG. 2.

In the potential melody line from step 780 are segments that do not belong to the melody. In melody pauses or between melody notes, dominant segments, e.g. found from the bass or other accompanying instruments. These melody pauses must be eliminated by the later steps in FIG. In addition, short, individual elements that can not be assigned to any area of the title arise. They are removed, for example, by means of a 3x3 average filter, as will be described below.

After the determination of the potential melody line in step 780, a general segmentation 782 is first performed in a step 782 which ensures that parts of the potential melody line are eliminated which prima facie can not belong to the actual melody line. For example, in FIG. 9, the result of the melody line determination of step 780 is shown by way of example for the case of the perceptual spectrum of FIG. 8. 9 shows the melody line plotted over the time t or over the sequence of frames along the x-axis, the frequency f and the frequency bins being indicated along the y-axis. In other words, in Fig. 9, the melody line of step 780 is represented in the form of a binary image array, which is also sometimes referred to as a melody matrix and has one row for each frequency bin and one column for each frame. All points of the array, on where the melody line is not located have a value of 0 or are white, while the points of the array where the melody line is located have a value of 1 or are black. These points are thus located on frequency bin and frame tuples, which are associated with each other by the melody line function of step 780.

Referring now to the melody line of FIG. 9, which is designated by reference numeral 784 in FIG. 9, general segmentation step 782, for which a possible implementation will be described with reference to FIG. 10, now operates.

The general segmentation 782 begins in a step 786 with the filtering of the melody line 784 in the frequency / time domain in a representation in which the melody line 784 as shown in Fig. 9 is shown as a binary track in an array represented by the frequency bins on the one and the frames are spanned on the other side. For example, let the pixel array of Fig. 9 be an x by y pixel array, where x is the number of frames and y is the number of frequency bins.

Step 786 is now intended to remove smaller outliers or artifacts in the melody line. 11 shows by way of example in schematic form a possible course of a melody line 784 in a representation according to FIG. 9. As can be seen, the pixel array shows regions 788 in which there are isolated black pixel elements, the sections of the potential melody line 784 determined by their temporal brevity not part of the actual tune and should therefore be removed.

Therefore, in step 786, a second pixel array is first generated from the pixel array of FIG. 9 or FIG. 11, in which the melody line is represented in binary form, by entering a value for each pixel, that of the summation of the binary values at the corresponding pixel as well as the pixel adjacent to that pixel. For this purpose, reference is made to FIG. 12a. There is shown an exemplary section of the course of a melody line in the binary image of Fig. 9 or Fig. 11. The exemplary portion of Figure 12a includes five rows corresponding to different frequency bins 1-5 and five columns A-E corresponding to different adjacent frames. The course of the melody line is symbolized in FIG. 12a by the hatching of the corresponding pixel elements representing parts of the melody line. According to the exemplary embodiment of FIG. 12a, therefore, the frequency bin 4 is assigned to the frame B by the melody line, the frequency bin 3, etc., to the frame C. Of course, the frame A is also assigned a frequency bin by the melody line, but this is not among the five frequency bins from the section of FIG. 12a.

In the filtering in step 786, the binary value of the same as well as the binary value of the neighboring pixels is first summed up for each pixel 790, as already mentioned. This is exemplified, for example, in FIG. 12a for the pixel 792, in which figure at 794 a square is drawn, which surrounds the pixel adjacent to the pixel 792 and the pixel 792 itself. Thus, for the pixel 792, there would be a sum of 2, because in the area 794 around the pixel 792 there are only 2 pixels, the belong to the melody line, namely the pixel 792 itself and the pixel C3, ie at the frame C and the bin 3. This summation is repeated by shifting the area 794 for all other pixels, resulting in a second pixel image, sometimes also referred to as Intermediate matrix called.

This second pixel image is then subjected to a pixel-by-pixel mapping, wherein in the pixel image all summation values from 0 or 1 to zero and all summation values greater than or equal to 2 are mapped to one. The result of this mapping is shown for the exemplary case of FIG. 12a in FIG. 12a with numbers of "0" and "1" in the individual pixels 790. As can be seen, the combination of 3x3 summation and subsequent mapping to "0" and "1" causes the melody line to "smear" by means of the threshold value 2. The combination acts as a low-pass filter, which would be undesirable in the context of step 786, the first pixel image, ie that of FIG. 9 or FIG. 11, or in FIG. 12a the pixel image, which is illustrated by the hatched pixels, with the second pixel array, ie the one in FIG This multiplication prevents low-pass filtering of the melody line by the filtering 786 and further ensures the uniqueness of the assignment of frequency bins to frames.

The result of the multiplication for the portion of FIG. 12a is that the filtering 786 does not change anything on the melody line. This is also desirable at this point since the melody line is obviously contiguous in this area and the filtering from step 786 is only yes - -

intended to eliminate outliers or artifacts 788.

For the purpose of illustrating the mode of action of the filtering 786, FIG. 12b therefore shows a further exemplary section from the melody matrix of FIG. 9 or FIG. 11. As can be seen there, the combination of summation and threshold value mapping results in an intermediate matrix in which two individual pixels P4 and R2 have a binary value of 0, although at these pixel positions the melody matrix has a binary value of 1, as can be seen by the hatching in Fig. 12b, which is intended to illustrate that the melody line is at these pixel positions , These isolated "outliers" of the melody line are therefore removed by the filtering in step 786 after multiplication.

After step 786, general segmentation 782 is followed by step 796, in which portions of melody line 784 are removed by neglecting those portions of the melody line that are not within a predetermined frequency range. In other words, in step 796, the value range of the melody line function is restricted to the predetermined frequency range from step 780. In other words, in step 796, all the pixels of the melody matrix of Fig. 9 and Fig. 11, respectively, are set to zero which are outside the predetermined frequency range. In the case of a polyphonic analysis, as assumed herein, a frequency range is, for example, 100-200-2001-10000 Hz, and preferably 150-1050 Hz. In the case of monophonic analysis as described with reference to FIGS. 27 ff is accepted Frequency range for example from 50-150 to 1,000-1,100 Hz and preferably from 80 to 1,050 Hz. The limitation of the frequency range on this bandwidth contributes to the observation that melodies in popular music are usually represented by singing, which is in this frequency range as well the human language.

To illustrate step 796, a frequency range of 150 to 1050 Hz is indicated by a lower cutoff frequency line 798 and an upper cutoff frequency line 800 in FIG. 9 by way of example. FIG. 13 shows the melody line filtered by step 786 and clipped by step 796, denoted by reference numeral 802 for discrimination in FIG.

After step 796, in a step 804, removal of sections of the melody line 802 with too small an amplitude takes place, wherein the extraction device 304 uses the logarithmic spectrum of FIG. 5 from step 770. More specifically, for each tuber of frequency bin and frame through which melody line 802 passes, extractor 304, in the logarithmic spectrum of FIG. 5, looks up the corresponding logarithmic spectral value and determines whether the corresponding logarithmic spectral value is less than a predetermined percentage is the maximum amplitude or the maximum logarithmic spectral value in the logarithmic spectrum of FIG. 5. In the case of polyphonic analysis, this percentage is preferably between 50 and 70% and preferably 60%, while in monophonic analysis this percentage is preferably between 20 and 40% and preferably 30%. Parts of the melody line 802 for which this is the case is neglected. This approach takes into account the fact that a melody usually always has approximately the same volume, or that sudden extreme volume fluctuations are unlikely to be expected. In other words, in step 804, all the pixels of the melody matrix of Figures 9 and 17, respectively, are set to zero at which the logarithmic spectral values are less than the predetermined percentage of the maximum logarithmic value.

Step 804 is followed, in a step 806, by a separation of those sections of the remaining melody line at which the course of the melody line in the frequency direction changes abruptly so as to have only a short halfway uniform melody curve. To illustrate this, reference is made to FIG. 14, which shows a portion of the melody matrix across A-M consecutive frames, with the frames arranged in columns as the frequency increases from bottom to top along the column direction. The frequency bin resolution is not shown in FIG. 14 for the sake of clarity.

The melody line, as it has resulted from step 804, is indicated by the reference numeral 808 in FIG. 14 by way of example. As can be seen, the melody line 808 in the frames AD remains constant on a frequency bin, to then show a frequency hopping between the frames D and E that is greater than a semitone distance HT. Between the frames E and H, the melody line 808 then remains constant again on a frequency bin, in order then to fall back from frame H to frame I by more than one semitone interval HT. Such a frequency hopping, which is greater than a semitone distance HT, also occurs between the frames J and K. Thenceforth - -

the melody line 808 between frames J and M again remains constant on a frequency bin.

To carry out the steps 806, the device 304 now scans the melody line in a frame-wise manner, for example from the front to the back. In this case, the device 304 checks for each frame whether a frequency jump greater than the semitone distance HT takes place between this frame and the subsequent frame. If so, means 304 marks these frames. In FIG. 14, the result of this marking is exemplarily illustrated by the fact that the corresponding frames are surrounded by a circle, here the frames D, H and J. In a second step, the means 304 checks between which of the marked frames less than one predetermined number of frames are arranged, wherein in the present case, the predetermined number is preferably three. Overall, this extracts portions of the melody line 808 where the same jumps between immediately consecutive frames less than a semitone but is less than four frame elements long. Between frames D and H there are three frames in the present exemplary case. This means nothing else than that the melody line 808 does not jump over the frames E - H by more than a semitone. However, there is only one frame between the marked frames H and J. This means nothing else than that in the area of the frames I and J, the melody line 808 jumps more than one semitone both forward and backward in the time direction. This section of the melody line 808, namely in the area of the frames I and J, is therefore neglected in the subsequent processing of the melody line. In the current melody matrix, therefore, the corresponding melody line element is set to zero at frames I and J, ie it is getting white. This exclusion can therefore comprise at most three consecutive frames, which corresponds to 24 ms. However, tones shorter than 30 ms rarely occur in today's music, so that the exclusion after step 806 does not lead to a deterioration of the transcription result.

After step 806, processing within general segmentation 782 proceeds to step 810, where device 304 divides the remaining remnants of the former potential melody line from step 780 into a sequence of segments. In the division into segments, all elements in the melody matrix are combined into a segment or a trajectory, which are directly adjacent. To illustrate this, FIG. 15 shows a portion of the melody line 812 as it appears after step 806. Only the individual matrix elements 814 from the melody matrix along which the melody line 812 extends are shown in FIG. For example, to check which matrix elements 814 are to be segmented, the device 304 scans them in the following manner. The device 304 first of all checks whether the melody matrix at all has a marked matrix element 814 for a first frame. If not, means 304 proceeds to the next matrix element and again checks the next frame for the presence of a corresponding matrix element. Otherwise, that is, if a matrix element that is part of the melody line 812 is present, the device 304 checks the next frame for the presence of a matrix element that is part of the melody line 812. If so, means 304 further checks if that matrix element is directly adjacent to the matrix element of the previous frame. Immediately adjacent is a matrix element to a _

others if they are directly adjacent in the row direction, or if they are diagonally corner to corner. If there is a neighborhood relationship, means 304 performs the check for the presence of a neighborhood relationship also for the next frame. Otherwise, i. in the absence of a neighborhood relationship, a currently recognized segment ends at the previous frame, and a new segment begins at the current frame.

The section of the melody line 812 shown in FIG. 15 represents an incomplete segment, in which all the matrix elements 814 that are part of the melody line or along which it runs are immediately adjacent to one another.

The segments found in this way are numbered consecutively, resulting in a sequence of segments.

The result of the general segmentation 782 is thus a sequence of melody segments, each melody segment covering a sequence of immediately adjacent frames. Within each segment, the melody line jumps from frame to frame by at most a predetermined number of frequency bins, in the preceding exemplary embodiment by at most one frequency bin.

After the general segmentation 782, the device 304 proceeds with the melody extraction at step 816. Step 816 is to close the gap between adjacent segments to address the case that, due to, for example, percussive events in the melody line determination in step 780, others accidentally Sound components have been detected and filtered out in the general segmentation 782. Gap closure 816 will be explained in greater detail with reference to FIG. 16, wherein gap closure 816 relies on a halftone vector, which is determined in a step 818, the determination of the halftone vector will be explained in more detail with reference to FIG.

Since the gap closure 816 uses the halftone vector, the determination of the variable halftone vector is first explained below with reference to FIG. 17. FIG. 17 shows the patchy melody line 812 resulting from the general segmentation 782 in a shape plotted in the melody matrix. Upon determination of the halftone vector in step 818, means 304 now determines which frequency bins the melody line 812 traverses and how many frames. The result of this approach, illustrated with the case 820, is a histogram 822 indicating, for each frequency bin f, the frequency with which it is traversed by the melody line 812 and how many matrix elements of the melody matrix that are part of the melody line 812 , are arranged at the respective Frequenzbin. From this histogram 822, device 304 then determines in a step 824 the frequency bin with the maximum frequency. This is indicated by an arrow 826 in FIG. Starting from this frequency bin 826 of the frequency fo, the device 304 then determines a vector of frequencies fi which have a frequency spacing to one another and above all to the frequency fo, which corresponds to an integer multiple of a half-tone length HT. The frequencies in the halftone vector will be referred to as halftone frequencies hereinafter. Sometimes the following will also be on Halftone cutoff frequencies referenced. These are located exactly between adjacent halftone frequencies, ie exactly centered on this. A halftone interval, as is customary in music, is defined as 2 ^{1/12 of} the ^frequency of use fo. By determining the halftone vector in step 818, the frequency axis f along which the frequency bins are plotted can be divided into halftone regions 828 extending from halftone cutoff frequency to the adjacent halftone cutoff frequency.

This division of the frequency axis f into halftone areas is based on the gap closure, which will be explained below with reference to FIG. 16. As previously mentioned, gap closure 816 attempts to close gaps between adjacent segments of melody line 812 that unintentionally resulted in melody line recognition 780 and general segmentation 782, respectively, as described above. The gap closure is carried out segment by segment. For a current reference segment, within gap closure 816, it is first determined in a step 830 whether the gap between the reference segment and the subsequent segment is less than a predetermined number of p frames. By way of example, FIG. 18 shows a section of the melody matrix with a section of the melody line 812. In the exemplary case, the melody line 812 has a gap 832 between two segments 812a and 812b, of which the segment 812a is the aforementioned reference segment. As can be seen, the gap in the exemplary case of FIG. 18 is six frames.

In the present exemplary case, with the preferred sampling frequencies, etc., given above, p is preferably 4. In the present case, the gap is 832 - -

that is, not less than four frames, whereupon the processing proceeds to step 834 to check if the gap 832 is less than or equal to q frames, where q is preferably 15. This is the case in the present case, so processing continues at step 836, where it is checked whether the facing segment ends of the reference segment 812a and the successor segment 812b, i. the end of the segment 812a and the beginning of the successor segment 812b lie in a same or adjacent halftone areas. In Fig. 18, to illustrate the facts, the frequency axis f is divided into halftone areas as determined in step 818. As can be seen, in the case of FIG. 18, the facing segment ends of the segments 812a and 812b lie in one and the same halftone area 838.

In this case, the positive 8 ^'REVIEW in step 836 within the processing proceeds to the gap-closing at step 840 continues, where it is checked, which amplitude difference in the perception-related spectrum of step 772 at the positions of the end of the reference segment 812a and the start of the successor segment 812b prevails. In other words, in step 840, in the perceptual spectrum of step 772, means 304 looks up the respective perceptual spectral values at the positions of the end of segment 812a and the beginning of segment 812b and determines the absolute value of the difference of the two spectral values. Further, means 304 determines in step 840 whether the difference is greater than a predetermined threshold r, preferably being 20-40% and preferably 30% of the perceptual spectral value at the end of the reference segment 812a. If the determination in step 840 gives a positive result, the gap closure proceeds to step 842. There, means 304 determines a gap closure line 844 in the melody matrix which directly connects the end of the reference segment 812a and the beginning of the successor segment 812b. The gap closure line is preferably rectilinear, as also shown in FIG. More specifically, the connecting line 844 is a function across the frames over which the gap 832 extends, the function assigning a frequency bin to each of these frames so that a desired connecting line 844 results in the melody matrix.

Along this line of connection, then, means 304 determines the corresponding perceptual spectral values from the perceptual spectrum from step 772 by looking up the respective frequency bin and frame tuples of gap closure line 844 in the perceptual spectrum. 3 ^'ber this perception-related spectral along the closing line Lücken¬ 304 determines the device the mean value and compares it as part of step 842 with the corresponding mean values of the perception-related spectral values along the reference segment 812a and the follower segment 812b. If both comparisons indicate that the mean for the gap closure line is greater than or equal to the average of the reference or successor segment 812a or b, then the gap 832 is closed in a step 846 by entering or closing the gap closure line 844 in the melody matrix the corresponding matrix elements thereof are set to 1. At the same time, in step 846, the list of segments to merge the segments 812a and 812b into a common segment, whereupon the gap closure for the reference segment and the successor segment is completed.

A gap closure along the gap closure line 844 also occurs if, at step 830, the gap 832 is less than 4 frames long. In this case, in a step 848, the gap 832 is closed, as in the case of step 846 along a direct and preferably straight gap closure line 844 connecting the facing ends of the segments 812a-812b, whereupon the gap closure for the two segments is finished and continues with the subsequent segment, as far as such exists. Although not shown in Fig. 16, the gap closure in step 848 will still be made conditional on that of step 836, i. that the two mutually facing segment ends lie in the same or adjacent halftone areas.

If any of the steps 834, 836, 840 or 842 result in a negative check result, the gap closure for the reference segment 812a ends and is performed again for the follower segment 812b.

The result of the gap closure 816 is thus a possibly shortened list of segments or a melody line, which may have gap closure lines in the melody matrix in some places. As was apparent from the previous discussion, at a gap of less than 4 frames, a connection between adjacent segments in the same or adjacent halftone area is always made. The gap closure 816 is followed by a harmony map 850, which is intended to eliminate errors in the melody line that have arisen by incorrectly determining the wrong root note of a sound in determining the potential melody line 780. In particular, the harmony mapping 850 operates segment by segment to shift individual segments of the melody line resulting from gap closure 816 by an octave, fifth, or major third, as will be described in more detail below. As the following description will show, the conditions for this are strict in order not to erroneously shift a segment wrong in frequency. The harmony mapping 850 will be described in more detail below with reference to FIGS. 19 and 20.

As already mentioned, the harmony mapping 850 is performed segment by segment. FIG. 20 shows by way of example a section of the melody line as it has appeared after the gap closure 816. This melody line is provided with the reference numeral 852 in FIG. 20, wherein in the section of FIG. 20 three segments can be seen from the melody line 852, namely the segments 852a-c. The representation of the melody line again takes place as a track in the melody matrix, it being recalled, however, that the melody line 852 is a function which uniquely assigns a single frequency bin to the individual frames - meanwhile not all of them - so that those shown in FIG Make tracks.

The segment 852b located between the segments 852a and 852c appears from the melody line progression as it would result from the segments 852a and 852c. to be cut out. In particular, in the present case, the segment 852b without frame gap follows the reference segment 852a by way of example, as indicated by a dashed line 854. Likewise, by way of example, the time range covered by the segment 852b is intended to be directly adjacent to the time range covered by the segment 852c, as indicated by a dashed line 856.

In FIG. 20, further dashed, dot-dashed and dot-dashed lines are shown in the melody matrix or in the time / frequency representation, which also result from a parallel displacement of the segment 852b along the frequency axis f. In particular, a dash-and-dot line 858 is four halftones, i. by a major third, shifted to segment 852b toward higher frequencies. Dashed line 858b is shifted down twelve halftones from frequency direction f, i. by an octave. Again a third line 858c is dot-dashed to this line and a fifth line 858d is a dot-and-dash line, i. a line shifted by seven semitones towards higher frequencies relative to line 858b.

As can be seen in Fig. 20, the segment 852b appears to have been erroneously detected in the context of the melody line determination 780, since the same would be less abruptly inserted between the adjacent segments 852a and 852c when shifted one octave lower. The task of the Harmoniemappings 850 is therefore to check whether a shift to such "outliers" should take place or not, since such frequency jumps occur less frequently in a melody. The harmony mapping 850 begins with the determination of a melody centroid line by means of a mean value filter in a step 860. In particular, step 860 comprises calculating a moving average of the melody curve 852 with a certain number of frames over the segments in the time direction t, the window length being 80 - 120, for example and preferably 100 frames at the above-mentioned frame length of 8 ms, ie correspondingly different number of frames at a different frame length. More precisely, to determine the melody centroid line, a window of length 100 frames is frame-shifted along the time axis t. In this case, all frequency bins associated with frames within the filter window by the melody line 852 are averaged and this mean value for the frame is entered in the middle of the filter window, which results in a melody center line 862 after repetition for successive frames in the case of FIG. a function that uniquely assigns a frequency to each frame. The melody centroid line 862 may extend over the entire time range of the audio signal, in which case the filter window at the beginning and end of the piece must be "compressed" accordingly, or only over a range from the beginning and end of the audio piece around the audio signal Half of the filter window width is spaced.

In a subsequent step 864, the device 304 checks whether the reference segment 852a is adjacent to the successor segment 852b along the time axis t. If this is not the case, the processing with the subsequent segment as the reference segment is performed again (866).

However, in the present case of FIG. 20, the check in step 864 results in a positive result, whereupon the processing proceeds to step 868. In step 868, the successor segment 852b is virtually shifted to the octave, fifth, and / or third lines 858a-d to obtain. The selection of major thirds, fifths and octaves is advantageous in pop music, as there is usually used a major chord, in which the highest and the lowest tone of a chord have a spacing of a major third plus a minor third of a fifth. Alternatively, the above procedure is of course also applicable to minor keys, in which chords of minor third and then major third occur.

In a step 870, then, means 304 in the spectrum evaluated with equal loudness curves or the perceptual spectrum from step 772, respectively, look up the minimum perceptual spectral value along reference segment 852a and the octave, quintet, and / or third line, respectively 858a-d. Thus, in the exemplary case of Fig. 20, there are five minimum values.

These minimum values are used in the subsequent step 872 to select one or none among the octave, fifth, and / or third shift lines 858a-d, depending on whether the octave, fifth, and / or octave shift lines or third-line minimum value has a predetermined reference to the minimum value of the reference segment. In particular, an octave line 858b is selected below the lines 858a-858d if the minimum value is at most 30% less than the minimum value for the reference segment 852a. A quint-line 858d is selected if the minimum value determined for it is at most 2.5% smaller than the minimum value of the reference segment 852a. One of the triplets 858c is used if the corresponding minimum value for that line is at least 10% greater than the minimum value for the reference segment 852a.

Of course, the above-mentioned values used as criteria for selection from lines 858a-858b can be varied, although they provided very good results for pop music pieces. Likewise, it is not absolutely necessary to set the minimum values for the reference segment or the individual lines 858a-d to determine, but it could, for example, the individual averages are used. The advantage of the diversity of the criteria for the individual lines is that this can take into account a probability that an octave, fifth or third jump has erroneously occurred in the melody line determination 780, or that such a jump in the melody was actually desired.

In a subsequent step 874, the device 304 shifts the segment 852b to the selected line 858a-858d, if such was selected in step 872, provided that the shift points in the direction of the melody centerline 862 as viewed from the follower segment 852b , In the exemplary case of Fig. 20, the latter condition would be satisfied unless the third line 858a were selected in step 872.

After the harmoniemapping 850, a vibrato detection and a vibrato compensation take place in a step 876, the mode of operation of which is explained in more detail with reference to FIGS. 21 and 27.

Step 876 is performed segment by segment for each segment 878 in the melody line as it results after harmony mapping 850. In FIG. 22, an exemplary segment 878 is shown enlarged, in a representation in which the horizontal axis corresponds to the time axis and the vertical axis corresponds to the frequency axis, as was the case in the previous figures. In a first step 880, the reference segment 878 is first examined in the context of the vibrato detection 876 for local extrema. Here again it is recalled that yes, the melody line function and thus also the part of the segment corresponding to the interest clearly maps the frames over this segment unambiguously on frequency bins in order to form the segment 888. This segment function is examined for local extrema. In other words, in step 880 examines the reference segment 878 for those points where it has local extremal parts along the time axis with respect to the frequency direction, ie, points where the slope of the melody line function is zero. These points are indicated in FIG. 22 by way of example with vertical lines 882.

In a subsequent step 884, it is checked whether the extrema sites 882 are arranged such that temporally adjacent local extreme parts 882 are arranged at frequency bins having a frequency spacing greater than or equal to a predetermined number of bins, for example 15 to 25 preferably but 22 bins with respect to implementation of the frequency analysis described in reference to FIG. 4, or a number of bins per semitone range of about 2 to 6, respectively. In FIG. 22, the length of 22 frequency bins is shown by way of example with a double arrow 886. As can be seen, the extremity parts 882 satisfy criterion 884.

In a subsequent step 888, the device 304 checks whether, between the adjacent extremum parts 882, the time interval is always less than or equal to a predetermined number of time frames, the predetermined number being 21, for example.

If the 3 ^'REVIEW in step 888 is positive, as in the example of FIG. 22 is the case, which is recognizable by the double arrow 890, which should correspond to the length of 21 frames, it is checked in a step 892, whether the The number of extremes 882 is greater than or equal to a predetermined number, which is preferably 5 in the present case. In the example of Fig. 22, this is given. Thus, even if the check in step 892 is positive, in a subsequent step 894 the reference segment 878 or the detected vibrato is replaced by its mean value. The result of step 894 is indicated at 896 in FIG. More specifically, in step 894, the reference segment 878 becomes is removed on the current melody line and replaced by a reference segment 896 which extends over the same frames as the reference segment 878 but runs along a constant frequency bin corresponding to the average of the frequency bins through which the replaced reference segment 878 passed. If the result of one of the fiber tests 884, 888 and 892 is negative, the vibrato detection or compensation ends for the relevant reference segment.

In other words, the vibrato detection and the vibrato balancing of Fig. 21 perform vibrato detection by stepwise feature extraction, which searches for local extrema, namely local minima and maxima, with a limitation on the number of allowed frequency bins of the modulation and a limitation in the modulation temporal distance of the extrema, where as a vibrato only a group of at least 5 extrema is considered. A recognized vibrato is then replaced in the melody matrix by its mean value.

After the vibrato detection in step 876, a statistical correction is performed in step 898, which also accounts for the observation that in a tune short and extreme pitch variations are not to be expected. The statistical correction of FIG. 898 will be further explained with reference to FIG. FIG. 23 shows, by way of example, a detail of a melody line 900, as may arise after the vibrato recognition 876. Again, the course of the melody line 900 is shown registered in the melody matrix, which is spanned by the frequency axis f and the time axis t. In the statistical correction 898, a melody centerline for the melody line 900 is first determined similar to the step 860 in the harmony mapping. For determination, as in the case of step 860, a window 902 of predetermined time length, such as also 100 frames in length, is frame-shifted along the time axis t to calculate, frame by frame, an average of the frequency bins that comprise the melody line 900 within the window 902, the average being assigned to the frame in the middle of the window 902 as a frequency bin, resulting in a point 904 of the melody centerline to be determined. The resulting melody centerline line is indicated by reference numeral 906 in FIG.

Then, a second window, not shown in FIG. 23, is frame-shifted along the time axis t, which has, for example, a window length of 170 frames. For each frame, the standard deviation of the melody line 900 to the melody center line 906 is determined. The resulting standard deviation for each frame is multiplied by 2 and added by 1 bin. This value is then added to and subtracted from each frame for each frequency bin passing through the melody centerline 906 at that frame to obtain upper and lower standard deviation lines 908a and 908b. The two standard deviation lines 908a and 908b define an allowed area 910 between them. As part of the statistical correction 898, all segments of the melody line 900 that are completely outside of the approval area 910 are now removed. The result of the statistical correction 898 is thus a reduction in the number of segments.

The step 898 is followed by a halftone mapping 912. The halftone mapping is performed frame-wise by resorting to the halftone vector, step 818, which defines the halftone frequencies. The halftone mapping 912 functions such that, for each frame on which the melody line resulting from step 898 is present, it is checked in which of the halftone areas the frequency bin lies in which the melody line passes through the respective frame Frequency bin the melody line function the respective frame maps. The melody line is then changed so that in the respective frame the melody line to the frequency value is changed corresponding to the semitone frequency of the semitone area in which the frequency bin was, through which the melody line was passed.

Instead of the frame-wise halftone mapping or quantization, it is also possible to carry out a segment-wise semitone quantization, for example by assigning only the frequency mean value per segment to one of the halftone areas and thus the corresponding halftone area frequency in the previously described manner, which is then used over the entire time length of the corresponding segment the frequency is used.

Steps 782, 816, 818, 850, 876, 898 and 912 thus correspond to step 760 in FIG. 2.

Upon halftone mapping 912, an onset detection and correction per segment is performed in step 914. This will be explained in more detail with reference to FIGS. 24-26.

The goal of the onset detection and correction 914 is to correct the individual segments of the melody line resulting from the halftone mapping 912, which correspond more and more to the individual notes of the searched tune, with respect to their starting times. For this purpose, the incoming or in step 750 provided audio signal 302 is used again, as will be described in more detail below.

In a step 916, first the audio signal 302 is filtered with a bandpass filter corresponding to the halftone frequency to which the respective reference segment has been quantized in step 912, or with a bandpass filter having cutoff frequencies between which the quantized halftone frequency of the respective segment lies , Preferably, the bandpass filter is used as one having cutoff frequencies corresponding to the halftone cutoff frequencies f _u and f _o correspond to the semitone area in which the considered segment is located. Still further preferably, as the band-pass filter, an IIR band-pass filter having the cut-off frequencies f _u and f ₀ associated with the respective halftone region is filtered as filter cutoff frequencies or with a Butterworth bandpass filter whose transfer function is shown in FIG.

Subsequently, in step 918, two-way rectification of the one filtered in step 916 is performed

Audio signal, whereupon, in step 920, the in step

918 received interpolated and the interpolated

Time signal is folded with a Hammingfenster, whereby an envelope of the two-way rectified and the filtered audio signal is determined.

Steps 916-920 are illustrated once again with reference to FIG. Fig. 26 shows by reference numeral 922 the two-way rectified audio signal, as it does after step 918, in a graph plotting horizontally the time t in virtual units and vertically the amplitude of the audio signal A in virtual units. Further, in the graph, the envelope 924 resulting in step 920 is shown.

Steps 916-920 are only one way of generating the envelope 924 and of course can be varied. In any case, envelopes 924 are generated for the audio signal for all those semitone frequencies or halftone areas in which segments or note segments of the current melody line are arranged. For each such envelope 924, the following steps of Fig. 24 are then performed.

First, in a step 926, potential start times are determined as the locations of locally maximum rise of the envelope 924. In other words, inflection points in the envelope 924 are determined in step 926 certainly. The timings of the inflection points in the case of FIG. 26 are illustrated with vertical bars 928.

For the following evaluation of the determined potential starting times or potential increases, a downsampling to the time resolution of the preprocessing is carried out, possibly in the context of step 926, which is not shown in FIG. 24. It should be noted that in step 926 not all potential start times or all inflection points have to be determined. It is also not necessary that all determined or determined potential starting times must be supplied to the subsequent processing. Rather, it is possible to determine or further process only those inflection points as potential starting times, which are arranged in temporal proximity before or in a time range which corresponds to one of the segments of the melody line arranged in the semitone area, which determines the determination of the melody line Envelope 924 was used.

In a step 928 it is now checked whether, for a potential start time, it lies before the segment start of the same corresponding segment. If so, processing continues at step 930. Otherwise, however, i. if the potential start time is past the existing start of the segment, step 928 is repeated for a next potential start time, or step 926 for a next envelope determined for another half-tone range, or segmented onset detection and correction is performed for a next segment ,

In step 930, it is checked whether the potential start time is more than x frames before the start of the corresponding segment, where x is between 8 and 12, inclusive, and preferably 10 with a frame length of 8 ms, the values for other frame lengths would be changed accordingly. If this is not the case, ie if the potential start time or the determined start time is 10 frames before the segment of interest, the gap between the potential start time and the previous segment start is closed or the previous start of the segment is corrected to the potential start time in a step 932 , If necessary, the predecessor segment is correspondingly shortened or its segment end is changed to the frame before the potential start time. In other words, step 932 includes extending the reference segment forward to the potential start time and possibly shortening the length of the precursor segment at the end thereof to avoid overlapping the two segments.

However, if the check at step 930 indicates that the potential start time is closer than x frames before the beginning of the corresponding segment, it is checked in step 934 whether step 934 is being traversed the first time for that potential start time. If this is not the case here, the processing for this potential start time and the relevant segment ends here, and the processing of the onset detection continues in step 928 for another potential start time or in step 926 for a further envelope.

Otherwise, however, in a step 936, the previous segment start of the segment of interest is virtually moved forward. In doing so, the perceptual spectral values in the perception-related spectrum are looked up, which are located at the virtually shifted segment start times. If the decrease of these perceptual spectral values in the perceptual spectrum exceeds a certain value, then the frame on which this transgression has taken place is provisionally used as segment start of the reference segment and step 930 is repeated again. Is then the potential start time not more than x frames before the beginning of the corresponding segment determined in step 936, the gap is also closed in step 932, as described above.

The effect of the onset detection and correction 914 is consequently that individual segments in the current melody line are changed in their time extent, namely extended forward or shortened at the back.

The step 914 is then followed by a length segmentation 938. In length segmentation 938, all segments of the melody line, which now appear as horizontal lines due to halftone mapping 912 in the melody matrix, which are at half tone frequencies, and those segments smaller than a predetermined length are removed from the melody line. For example, segments are removed that are less than 10-14 frames long, and preferably 12 frames and less long, again assuming a frame length of 8ms above or adjusting the numbers of frames accordingly. 12 frames at 8 milliseconds correspond to 96 milliseconds time resolution, which is less than about 1/64 note.

Steps 914 and 938 thus correspond to step 762 of FIG. 2.

The melody line held in step 938 then consists of a somewhat reduced number of segments having one and the same semitone frequency over a certain number of consecutive frames. These segments are clearly attributable to musical segments. This melody line is then converted to a note representation in a step 940 corresponding to the above-described step 764 of FIG. 2, or into a midi file. In particular, each segment still in the melody line after the length segmentation 938 is examined to include the first frame in FIG to find the respective segment. This frame then determines the note start time of the note corresponding to that segment. For the note, the note length is then determined from the number of frames over which the corresponding segment extends. The quantized pitch of the note results from the halftone frequency, which is constant in each segment due to step 912.

The MIDI output 914 by means 304 then provides the note sequence, based on which the rhythm means 306 performs the operations described above.

The foregoing description, referring to FIGS. 3-26, referred to the melody recognition in the device 304 in the case of polyphonic audio pieces 302. However, it is known that the audio signals 302 are of monophonic type, as in the case of the pre-hum and Whistle for generating ring tones, as has been described above, is the case, one may be compared to

3 may be preferred in that it may avoid errors that may result from the approach of FIG. 3 due to musical imperfections in the source audio signal 302.

Fig. 27 shows the alternative operation of the device

304, which for monophonic audio signals compared to the

3 approach is preferable, but in principle also for polyphonic audio signals would be applicable.

Until the step 782, the procedure of Fig. 27 is the same as that of Fig. 3, and therefore, the same reference numerals as in the case of Fig. 3 are used for these steps.

In contrast to the procedure according to FIG. 3, after the step 782 in the procedure according to FIG -

Sound separation performed in step 950. The reason for performing the tone separation in step 950, which will be explained in more detail with reference to FIG. 28, can be illustrated with reference to FIG. 29, which is a section of the frequency / time space of the spectrogram of the audio signal , ^■, illustrating texture of the spectrogram, as it results after the frequency analysis 752 for a predetermined segment of the melody line 952, as it results after the general segmentation 782 as the root and for their harmonics. In other words, in Fig. 29, the exemplary segment 952 has been shifted along the frequency direction f by integer multiples of the respective frequency to determine harmonic lines. FIG. 29 now shows only those parts of the reference segment 952 and corresponding overtone lines 954a-g at which the spectrogram from step 752 has spectral values that exceed an exemplary value.

As can be seen, the amplitude of the fundamental of the reference segment 952 obtained in the general segmentation 782 is consistently above the exemplary value. Only the overtones arranged above indicate an interruption approximately in the middle of the segment. The patency of the root has caused the segment in general segmentation 782 not to split into two notes, although there is likely to be a note boundary at about the middle of segment 952. Errors of this kind occur mainly only in monophonic music, for which reason the sound separation is performed only in the case of Fig. 27.

The sound separation 950 will now be explained in more detail below with reference to FIGS. 28, 29 and 30 a, b. The tone separation begins at step 958, starting from the melody line obtained in step 782, with the search for that overtone or tone lines 954a-954g along which the spectrogram obtained by frequency analysis 752 has the most dynamic amplitude response. FIG. 30a shows in a graph in which the x- The axis of a time axis t and the y-axis of the amplitude or the value of the spectrogram corresponds, by way of example, to such an amplitude curve 960 for one of the overtone lines 954a-954g. The dynamics for the amplitude curve 960 is determined from the difference between the maximum spectral value of the curve 960 and the minimum value within the curve 960. Fig. 30a will exemplify the amplitude curve of the spectrogram along that overtone line 450a - 450g, which has the greatest dynamics among all these amplitude curves. At step 958, preferably only the 4th through 15th order overtones are considered.

In a following step 962, in the amplitude progression with the greatest dynamics, those points at which a local amplitude minimum falls below a predetermined threshold are then identified as potential separation points. This is illustrated in Fig. 30b. In the exemplary case of FIGS. 30a and b respectively, only the absolute minimum 964, which of course also represents a local minimum, falls below the threshold value, which is illustrated in FIG. 30b by way of example with the dashed line 966. Thus, in Fig. 30b there is only one potential separation point, namely the time or frame at which the minimum 964 is located.

In a step 968, those which are located in a boundary area 970 around the segment start 972 or in a boundary area 974 around the segment end 976 are then sorted out among the possibly multiple separation sites. For the remaining potential separation points, in step 978 the difference between the amplitude minimum at the minimum 964 and the mean of the amplitudes of the local maxima 980 and 982 adjacent to the minimum 964 is formed in the amplitude curve 960. The difference is illustrated in Fig. 30b with a double arrow 984. In a subsequent step 986, it is checked whether the difference 984 is greater than a predetermined threshold value. Otherwise, in a step 988, the reference segment at the potential separation point or minimum 964 is separated into two segments, which is one of the segment start 972 extends to the frame of the minimum 964, and the other between the frame of the minimum 964 and the subsequent frame and the segment end 976. The list of segments is extended accordingly. Another way of separating 988 is to provide a gap between the two emerging segments. For example, in the area in which the amplitude curve 960 is below the threshold value-in FIG. 30b, for example, over the time area 990.

Another problem that occurs primarily in monophonic music is that the individual notes are subject to frequency fluctuations that complicate subsequent segmentation. Therefore, subsequent to the sound separation 950, sound smoothing is performed in step 992, which is explained in more detail with reference to FIGS. 31 and 32.

Fig. 32 shows, in high magnification, schematically a segment 994, as it is in the melody line, which results in the sound separation 950 out. The illustration in FIG. 32 is such that in FIG. 32, a digit is provided to the corresponding tuple for each frequency bin and frame tuple traversed by the segment 994. The assignment of the digit will be explained in more detail below with reference to FIG. 31. As can be seen, segment 994, in the exemplary case of FIG. 32, varies across 4 frequency bins and spans 27 frames.

The purpose of tone smoothing is now to place below the frequency bins, between which the segment 994 back and forth varies to select the one that is to be consistently assigned to segment 994 for all frames.

The tone smoothing begins in step 996 with the initialization of a counter variable i to 1. In a subsequent step 998, a counter value z is initialized to 1. The counter variable i has the meaning of the numbering of the frames of the segment 994 from left to right in Fig. 32. The counter variable z has the meaning of a counter that counts over how many consecutive frames the segment 994 is in one and the same frequency bin. In FIG. 32, to facilitate understanding of the following steps, the value for z for the individual frames is shown in the form of the numbers representing the course of the segment 994 in FIG.

In a step 1000, the counter value z is then accumulated to a sum for the frequency bin of the ith frame of the segment. For each frequency bin in which the segment 994 oscillates, there exists a sum or an accumulation value. In this case, the counter value could be weighted according to a varying exemplary embodiment, such as a factor f (i), where f (i) is a function that increases steadily with i, thus the shares to be totalized at the end of a segment, ie the voice, for example better tuned to the tone, to weight more strongly compared to the transient at the beginning of a note. Below the horizontal time axis, an example of such a function f (i) is shown in square brackets in FIG. 32, where in FIG. 32 i increases along the time and indicates how many positions a particular frame occupies among the frames of the considered segment , and successive values, which assumes the function shown by way of example for successive sections, which in turn are indicated by small vertical bars along the time axis, are shown with numbers in these square brackets. As can be seen, the exemplary weighting function increases with i from 1 to 2.2. In a step 1002, it is checked if the i-th frame is the last frame of the segment 994. If this is not the case, the counter variable i is incremented in a step 1004, ie it is moved to the next frame. In a subsequent step 1006, it is checked whether the segment 994 in the current frame, ie the i-th frame, is in the same frequency bin as it was in the (i-1) th frame. If so, in a step 1008 the counter variable z is incremented, whereupon processing continues again at step 1000. However, if the segment 994 is not in the same frequency bin in the ith frame and the (il) th frame, processing continues with the initialization of the counter variable z to 1 in step 998.

Finally, if it is determined in step 1002 that i-th frame is the last frame of segment 994, then for each frequency bin in which segment 994 resides, a sum is shown at 1010 in FIG.

In step 1012, upon determination of the last frame in step 1002, the frequency bin for which the accumulated sum 1010 is greatest is selected. In the exemplary case of FIG. 32, this is the second lowest frequency bin among the four frequency bins in which segment 994 resides. In a step 1014, the reference segment 994 is then smoothed by swapping it to a segment in which each of the frames where the segment 994 was located is assigned the selected frequency bin. The tone smoothing of FIG. 31 is repeated in segments for all segments.

In other words, tone smoothing, in other words, serves to equalize the singing and singing of sounds from lower or higher frequencies, and accomplishes this by finding a value over time a tone corresponding to the frequency of the settled sound. To determine the frequency value of the oscillating signal, all elements of a frequency band are counted up, after which all the incremented elements of a frequency band, which are located on the note segment, are added up. Then the tone is entered over the time of the note segment in the frequency band with the largest sum.

After the tone smoothing 992, a statistical correction 916 is then carried out, wherein the implementation of the statistical correction corresponds to that of FIG. 3, namely in particular to step 898. The statistical correction 1016 is followed by a halftone mapping 1018, which corresponds to the halftone mapping 912 of FIG. 3 and also uses a halftone vector, which is determined at a halftone vector detection 1020, which corresponds to that of FIG. 818.

Steps 950, 992, 1016, 1018 and 1020 thus correspond to step 760 of FIG. 2.

The halftone mapping 1018 is followed by an onset identifier 1022, which essentially corresponds to that of FIG. 3, namely step 914. Only in step 932 is it preferably prevented that gaps are closed again, or segments imposed by the tone separation 950 are closed again.

The onset identifier 1022 is followed by offset detection and correction 1024, which is explained in more detail below with reference to FIGS. 33-35. In contrast to the onset recognition, the offset detection and correction is used to correct the end of the note. The offset detection 1024 serves to prevent the reverberation of monophonic pieces of music. In a step 1026 similar to step 916, first the audio signal is filtered with a bandpass filter corresponding to the halftone frequency of the reference segment, whereupon, in a step 1028 corresponding to step 918, the filtered audio signal is two-way rectified. Furthermore, an interpretation of the rectified time signal is performed in step 1028. This approach is sufficient in the case of offset detection and correction to determine approximately an envelope, thereby eliminating the more complicated step 920 of onset detection.

34 shows in a graph in which along the x-axis the time t is plotted in virtual units and along the y-axis of the amplitude A in virtual units, the interpolated time signal for example with a reference numeral 1030 and for comparison the envelope, as determined in the onset recognition in step 920, with a reference numeral 1032.

In a step 1034, in the time segment 1036 corresponding to a reference segment, a maximum 1040 of the interpolated time signal 1030 is determined, in particular the value of the interpolated time signal 1030 at the maximum 1040. In a step 1042, a potential note end time is then determined as the time wherein the rectified audio signal has dropped in time to the maximum 1040 to a predetermined percentage of the value at the maximum 1040, the percentage in step 1042 being preferably 15%. The potential note end is illustrated in FIG. 34 with a dashed line 1044.

In a subsequent step 1046, it is then checked whether the potential note end 1044 is behind the segment end 1048 in time. If this is not the case, as shown by way of example in FIG. 34, then the reference segment is shortened by the time range 1036 in order to be able to potential end of note 1044 to end. However, if the note end is timed before the end of the segment, as shown by way of example in FIG. 35, it is checked in step 1050 if the time interval between potential note end 1044 and segment end 1048 is less than a predetermined percentage of the current segment length a, where the predetermined percentage in step 1050 is preferably 25%. Falls the result of the review

1050 is positive, an extension 1051 of the reference segment of length a takes place to now terminate at the potential note end 1044. To prevent overlap with the subsequent segment, the step may

1051, however, also be pending from a threatening Sberlappung, in order not to be carried out in this case, or just until the beginning of the successor segment, possibly with a certain distance to the same.

However, if the check in step 1050 is negative, no offset correction is performed and step 1034 and the following steps are repeated for another reference segment of equal semitone frequency or step 1026 for other semitone frequencies is continued.

After offset detection 1024, in step 1052, a length segmentation corresponding to step 938 of FIG. 3 is performed

1052, followed by a MIDI output 1054 corresponding to step 940 of FIG. Step 762 of FIG. 2 corresponds to steps 1022, 1024, and 1052.

With reference to the preceding description of Figs. 3-35, reference is made to the following. The two alternative melody extraction approaches presented herein include various aspects that need not all be included simultaneously in an effective melody extraction approach. It should first be pointed out that, in principle, steps 770-774 could also be combined with each other by the spectral values of the spectrogram from the frequency analysis 752 be transformed into the perceptual spectral values in a lookup table by only a single lookup.

In principle, it would of course also be possible to omit steps 770-774 or only steps 772 and 774, but this should lead to a deterioration of the melody line determination in step 780 and thus to a deterioration in the overall result of the melody extraction method as a whole.

In basic frequency determination 776, a tone model from Goto was used. However, other tone models or other weighting of the overtone components would also be possible and could be adapted, for example, to the origin or the origin of the audio signal, as far as the latter is known, such as e.g. In the embodiment of the ringtone generation, the user is set to a pre-buzz.

With regard to the determination of the potential melody line in step 780, it is pointed out that although the above-mentioned musicological statement states that for each frame only the fundamental frequency of the loudest sound component had been selected, it is still possible to choose not only one to narrow down the clear selection of the largest share for each frame. As with Paiva, for example, the determination of the potential melody line 780 could include assigning multiple frequency bins to the same frame. Subsequently, a finding of multiple trajectories could be performed. This means allowing a selection of multiple fundamental frequencies or multiple sounds for each frame. Of course, the subsequent segmentation would then have to be carried out differently and, in particular, the subsequent segmentation would be somewhat more complicated since several trajectories or segments would have to be considered and found. Conversely, in In this case, some of the above-mentioned steps or sub-steps in the segmentation for this case, the determination of trajectories that can overlap temporally, are adopted. In particular, the steps 786, 796 and 804 from the general segmentation could easily be applied to this case as well. Step 806 could be for the case where the melody line. from temporally overlapping trajectories, would be transmitted if this step took place after the trajectories were identified. The identification of trajectories could be similar to step 810, but modifications would have to be made such that multiple trajectories overlapping in time can also be tracked. The gap closure could also be carried out in a similar way for those trajectories between which there is no gap in time. Harmoniemapping could also be performed between two trajectories that follow one another directly in time. Vibrato detection and / or vibrato compensation could be readily applied to a single trajectory as well as to the previously mentioned non-overlapping melody line segments. Onset detection and correction could also be applied to trajectories. The same applies to tone separation and tone smoothing as well as offset detection and correction as well as statistical correction and length segmentation. Allowing the temporal overlap of trajectories of the melody line in the determination in step 780, making it at least requires, however, that prior to the actual note sequence output the temporal 8 ^'Overlap has to be once removed from trajectories at some point. The advantage of determining the potential melody line in the manner described above with reference to FIGS. 3 and 27 is that the number of segments to be examined after the general segmentation is limited in advance to the most essential, and that the melody line determination even in step 780 extremely is simple and yet leads to a good melody extraction or note generation or transcription.

The general segmentation implementation described above does not have to have all substeps 786, 796, 804, and 806, but may also include a selection thereof.

In gap closure, the perceptual spectrum was used in steps 840 and 842. In principle, however, it is possible in these steps, the logarithmic spectrum or directly from the

However, the use of the perceptual spectrum in these steps gives the best result in terms of the

Melody extraction has resulted. The same applies to the step

870 from the harmony mapping.

With regard to the harmoniemappings is pointed out that it could be provided there, in the displacement 868 of the successor segment, the shift equal only in

Direction of the melody centerline so that the second condition could be omitted in step 874. Referring to step 872, it is noted that uniqueness among the selection of the various octave, fifth, and / or third lines could be achieved by including one of them

Priority list is generated, such as Octave line in front

Quint line before the third line and below lines of the same line type (octave, quint, or third line) that is closer to the original position of the successor segment.

With regard to the onset detection and the offset detection, it is pointed out that the determination of the envelope or of the interpolated time signal used instead in the case of offset detection could also be carried out differently. All that is essential is that the onset and offset detection is based on the audio signal - -

which is filtered by a band pass filter having a pass characteristic around the respective halftone frequency to detect the note start time at the rise of the envelope of the resulting filtered signal, and the note end time from the fall of the envelope.

With respect to the flowcharts of FIGS. 8-41, it should be noted that they illustrate the operation of the melody extractor 304 and that each of the steps represented by a block in these flowcharts may be implemented in a corresponding subset of the device 304. The implementation of the individual steps can be realized in hardware, as an ASIC circuit part, or in software, as a subroutine. In particular, in these figures, the explanations inscribed in the blocks roughly indicate which operation the respective step corresponds to that of the respective block, while the arrows between the blocks illustrate the order of steps in the operation of the device 304.

In particular, it should be noted that, depending on the circumstances, the inventive scheme can also be implemented in software. The implementation may be on a digital storage medium, in particular a floppy disk or a CD with electronically readable control signals, which may cooperate with a programmable computer system such that the corresponding method is executed. In general, the invention thus also consists in a computer program product with program code stored on a machine-readable carrier for carrying out the method according to the invention when the computer program product runs on a computer. In other words, the invention can thus be realized as a computer program with a program code for carrying out the method when the computer program runs on a computer.

Claims

claims

1. A device for extracting an audio signal (302) underlying melody, with

means (750) for providing a time / spectral representation of the audio signal (302), wherein the means (750) for providing is adapted to provide a time / spectral representation including a spectral band for each of a plurality of spectral components having a sequence of spectral values, and that the time / spectral representation in each spectral band comprises a spectral value at each time portion of a sequence of time segments of the audio signal;

means (754, 758) for, on the basis of time / spectral representation of the audio signal (302), determining a melody line of the audio signal by uniquely assigning exactly that spectral component to each time segment for which the time / spectral representation or a version derived therefrom the time / spectral representation is maximum; and

means (760, 762) for, based on the melody line, determining the melody of the audio signal.

2. Apparatus according to claim 1, wherein the means for determining the melody line comprises:

means (754; 770, 772, 774) for scaling the time / spectral representation using equal volume curves reflecting the human volume perception to obtain a perceptual time / spectral representation; and -

means (760, 762) for, based on the perceptual time / spectral representation, determining the melody of the audio signal.

3. Apparatus according to claim 2, wherein the means for scaling comprises:

means (770) for logarithmizing the spectral values of the time / spectral representation to indicate the sound pressure level, thereby obtaining a logarithmized time / spectral representation; and

means (772) for mapping the logarithmized spectral values of the logarithmized time / spectral representation, depending on their respective value and the spectral component to which they belong, to perceptual spectral values to obtain the perceptual time / spectral representation.

4. Apparatus according to claim 3, wherein the device

(772) for mapping to perform the mapping based on functions (774) representing the equal volume curves, associating with each spectral component a logarithmic spectral value indicative of a sound pressure level and assigned to different volumes.

5. Apparatus according to claim 4, wherein the means (756) for determining the melody line of the audio signal is adapted to

the spectral values of the perceptual spectrum to Delogarithm (776) to obtain a delogarithmized perceptual spectrum with delogarithmized perceptual spectral values, - -

for each time period and for each spectral component, summing the delogarithmized perceptual spectral value of the respective spectral component and the delogarithmized perceptual spectral values to the spectral components representing a partial tone to the respective spectral component (776) to obtain a sound spectral value, thereby producing a time / sound representation is obtained, and

to produce (780) a melody line by uniquely assigning at each time period that spectral component for which the summation gives the largest sound spectral value for the corresponding period of time.

6. Apparatus according to claim 5, wherein the means for determining the melody line of the audio signal is adapted to different in the summations (780) the delogarithmierten perceptual spectral values of the respective spectral components and those of those spectral components that represent a partial sound to the respective spectral component so that the delogarithmized perceptual spectral values of higher order partial tones are weighted less.

7. Apparatus according to claim 5 or 6, wherein the means for determining the melody of the audio signal comprises:

means (782,816,818,850,876,898,912,914,938,782,950,992,1016,1018,1020,1022,1024,1052) for segmenting the melody line (784) to segments receive.

An apparatus according to claim 7, wherein said means for segmenting is adapted to binary the binary line in a state that the melody line in a melody matrix is made up of matrix positions spanned by the spectral components on one side and the time sections on the other side is prefiltered (786).

9. Apparatus according to claim 8, wherein the means for segmenting is adapted to pre-filter

(786) for each matrix position (792) to sum the entry in that and adjacent matrix positions, compare the resulting information value to a threshold, and register the comparison result to a corresponding matrix position in an intermediate matrix, and then to multiply the melody matrix and the intermediate matrix to obtain the melody line in prefiltered form.

Apparatus according to any of claims 6 to 9, wherein the means for segmenting is adapted to disregard (796) a portion of the melody line during a subsequent portion of the segmentation that is outside of a predetermined spectral range (798, 800) ,

11. The device according to claim 10, wherein the means for segmenting is formed such that the predetermined spectral range of 50 - 200 Hz from 1000 to 1200 Hz enough.

Apparatus according to any of claims 7 to 11, wherein the means for segmenting is adapted to disregard a portion of the melody line in a subsequent portion of the segmentation (804) at which the logarithmized time / spectral representation comprises logarithmized spectral values that is less than a predetermined percentage - -

of the maximum logarithmic spectral value of the logarithmized time / spectral representation.

13. Device according to one of claims 7 to 12, wherein the means for segmenting is formed to

Disregarding portions of the melody line in a subsequent portion of the segmentation at which, according to the melody line, less than a predetermined number of spectral components assigned to adjacent periods have a pitch less than a semitone pitch from one another.

14. Device according to one of claims 10 to 13, wherein the means for segmenting is formed around the melody line reduced by the unconsidered parts

(812) into segments (812a, 812b), such that the number of segments is as small as possible and adjacent time segments of a segment according to the melody line are assigned to spectral components whose

Distance is less than a predetermined amount.

15. The device according to claim 14, wherein the means for segmenting is formed to

closing a gap (832) between adjacent segments (8a, 812b) to obtain a segment from the adjacent segments if the gap is less than a first number of time periods (830) and the time segments of the adjacent segments (12a, 812b) closest to the respective other of the adjacent segments, are assigned by the melody line spectral components which are in a same halftone area (838) or in contiguous halftone areas (836),

the gap in the case that the same though greater than or equal to the first number of periods but -

is less than a second number of time periods greater than the first number (834) to close the gap (846) only if

the time periods of the adjacent segments (12a,

812b) closest to the respective other of the adjacent segments, are assigned by the melody line spectral components which are in a same halftone area (838) or in contiguous halftone areas (836),

the perceptual spectral values at these time periods differ less than a predetermined threshold (840); and

an average of all perceptual spectral values along a connecting line (844) between the adjacent segments (812a, 812b) is greater than or equal to the average of the perceptual spectral values along the two adjacent segments (842).

16. The device according to claim 15, wherein the means for segmenting is adapted to in the context of

Segmentation to determine those spectral components (826) most frequently assigned to the time periods according to the melody line, and to determine (824) relative to this spectral component a set of halftones separated by halftone boundaries which in turn define the halftone areas (828) ,

17. The device according to claim 15 or 16, wherein the means is formed for segmenting to

closing the gap by means of a rectilinear connection line (844).

18. The device according to claim 14 to 17, wherein the means for segmenting is formed to

a successor segment (852b) of the segments leading to a

Reference segment (852a) of the segments is directly adjacent without an intervening period

(864), preliminary shift in spectrum direction

(868) to obtain an octave, fifth and / or third line;

selecting one or none of the octave, fifth and / or third line (872), depending on whether a minimum below the perceptual spectral values along the reference segment (852a) to a minimum below the perceptual spectral values along the octave, fifth and / or third line has a predetermined relationship; and

if one of the octave, fifth and / or third line is selected, the successor segment is finally shifted to the selected one of the octave, fifth and / or third line (874).

19. The device according to claim 14 to 18, wherein the means is formed for segmentation to

in a predetermined segment (878) of the melody line, determine all local extrema (882);

determine a sequence of contiguous extrema among the particular extremes for which all adjacent extrema are located at spectral components that are less than a first predetermined amount (886) apart, and at time periods less than a second predetermined measure (890) away from each other, and modifying the predetermined segment (878) such that the time periods of the sequence of extrema and the time periods between the sequence of extrema are assigned the mean value of the spectral components of the melody line at those time periods (894).

20. Device according to one of claims 14 to 1-9, wherein the means for segmenting is adapted to determine, in the context of the segmentation, that spectral component (832) which is most frequently assigned to the time segments according to the melody line, and relative thereto Spectral component (832) to determine a set of halftones separated by halftone boundaries, which in turn define the halftone areas, and wherein the means for segmenting is adapted to

for each period in each segment, change the spectral component assigned to it to a semitone from the set of half-tones (912).

The apparatus of claim 20, wherein the means for segmenting is adapted to make the change to the halftones such that this halftone is closest to the set of halftones of the spectral component to be changed.

22. The device according to claim 20 or 21, wherein the means for segmenting is formed to

filtering the audio signal with a bandpass filter (916) having a pass-through characteristic around the common semitone of a predetermined segment to obtain a filtered audio signal (922);

examine the filtered audio signal (922) (918, 920, 926) to determine at what times in an envelope (924) of the filtered audio signal (922) has inflection points, these times representing candidate start times;

depending on whether a predetermined candidate start time is less than a predetermined time before the first segment (928, -930), extending the predetermined segment forward by one or more further time slots (932) to obtain an extended segment ends approximately at the predetermined candidate start time.

23. The apparatus of claim 22, wherein the means for segmenting is configured to shorten a preceding segment as the segment extends (932) forward of the predetermined segment, thereby preventing overlap of the segments over one or more time periods.

24. The device according to claim 22 or 23, wherein the means for segmenting is formed to

in the perceptual time / spectral representation, the perceptual spectral values along an extension of the predetermined segment towards the candidate start time to a virtual one depending on whether the predetermined candidate start time is more than the first predetermined time period before the first time period of the predetermined segment Time (936) and then depending on whether the predetermined candidate start time is more than the first predetermined time period before the virtual time, the predetermined segment forward by one or more further time periods to extend (932) to the extended one Segment that ends at about the predetermined candidate start time.

Apparatus according to any one of claims 22 to 24, wherein said means for segmenting is arranged to discard (-938) segments shorter than a predetermined number of time periods after performing the filtering, detection and supplementation.

Apparatus according to any one of claims 7 to 25, further comprising means (940) for converting the segments into notes, the means for converting being arranged to assign to each segment a note start time corresponding to the first time portion of the segment Note duration, which corresponds to the number of time segments of the segment multiplied by a time period duration, and a pitch corresponding to an average of the spectral components that the segment traverses.

27. Device according to one of claims 14 to 26, wherein the device is designed for segmentation to

to determine a predetermined (952) of the segments of overtone segments (954a-g),

determine, among the overtone segments, the tone segment (958) along which the time / spectral representation of the audio signal has the greatest dynamics,

determine a minimum (964) in the history (960) of the time / spectral representation along the particular overtone segment (962); examining (986) if the minimum satisfies a predetermined condition, and

if so, separating the predetermined segment into two segments at the time period where the minimum is (988).

28. The apparatus of claim 27, wherein the means for segmenting is configured to determine the minimum (964) with an average of adjacent local maxima (980, 982) of the trace (960) when examining whether the minimum satisfies a predetermined condition ) compare (986) the time / spectral representation along the predetermined overtone segment and perform the separation (988) of the predetermined segment into the two segments in dependence on the comparison.

29. Device according to one of claims 14 to 28, wherein the means for segmenting is formed to

for a predetermined segment (994), assigning a number (z) to each period (i) of the segment, such that for all groups of immediately adjacent time segments to which the same spectral component is assigned by the melody line, the numbers assigned to the directly adjacent periods are different numbers from 1 to the number of directly adjacent time periods,

for each spectral component assigned to one of the time segments of the predetermined segment, adding up the numbers of those groups (1000) to which time periods the respective spectral component is assigned,

determine a smoothing spectral component as the spectral component (1012) that gives the largest accumulation; and changing (1014) the segment by assigning to each time segment of the predetermined segment the particular smoothing spectral component.

30. Device according to one of claims 14 to 29, wherein the means for segmenting is formed to

filtering the audio signal with a bandpass filter (1026) having a bandpass around the common semitone of a predetermined segment to obtain a filtered audio signal;

in an envelope of the filtered audio signal, locate a maximum (1040) in a time window (1036) corresponding to the predetermined segment (1034);

determining (1042) a potential segment end at which the envelope has dropped to the maximum (1040) for the first time to a value that is less than a predetermined threshold,

if the potential segment end is earlier than an actual segment end of the predetermined segment (1046), shortening the predetermined segment (1049).

31. The apparatus of claim 30, wherein the means for segmenting is formed to

if the potential segment end is later than the actual segment end of the predetermined segment (1046), extending the predetermined segment (1051) if the time interval between the potential segment end (1044) and the actual segment end (1049) is not greater than a predetermined threshold is (1050).

32. Apparatus according to any one of the preceding claims, wherein the means (756) for determining the melody line of the audio signal is adapted to

for each period and for each spectral component, summing the spectral value of the respective spectral component or a scaled spectral value obtained therefrom by scaling and the spectral values to those spectral components which are a partial tone to the respective spectral component or summing scaled spectral values obtained therefrom by scaling (776) to obtain a sound spectral value, whereby a time / sound representation is obtained, and

to produce (780) a melody line by uniquely assigning at each time period that spectral component for which the summation gives the largest sound spectral value for the corresponding period of time.

33. A method for extracting a melody based on an audio signal (302), comprising

Providing (750) a time / spectral representation of the audio signal (302) such that a time / spectral representation is provided having a spectral band having a sequence of spectral values for each of a plurality of spectral components, and the time / spectral representation in each spectral band has a spectral value at each time portion of a sequence of time segments of the audio signal;

based on time / spectral representation of the audio signal

(302), finding (754, 758) a melody line of the

Audio signal by, uniquely assigning exactly that spectral component at each time period, for which the time / spectral representation at the respective time segment has the largest spectral value; and

based on the melody line, determining (760, 762) the melody of the audio signal.

34. A computer program having a program code for carrying out the method according to claim 33, when the computer program runs on a computer.