Connect public, paid and private patent data with Google Patents Public Datasets

Robust and invariant audio pattern matching

Download PDF

Info

Publication number
US20090265174A9
US20090265174A9 US10978313 US97831304A US2009265174A9 US 20090265174 A9 US20090265174 A9 US 20090265174A9 US 10978313 US10978313 US 10978313 US 97831304 A US97831304 A US 97831304A US 2009265174 A9 US2009265174 A9 US 2009265174A9
Authority
US
Grant status
Application
Patent type
Prior art keywords
audio
fingerprint
relative
time
objects
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
US10978313
Other versions
US7627477B2 (en )
US20050177372A1 (en )
Inventor
Avery Wang
Daniel Culbert
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shazam Investments Ltd
Original Assignee
Landmark Digital Services LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496Recognising patterns in signals and combinations thereof
    • G06K9/00536Classification; Matching
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H1/00Details of electrophonic musical instruments
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/02Feature extraction for speech recognition; Selection of recognition unit
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • G10L15/10Speech classification or search using distance or distortion measures between unknown speech and reference templates
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H2240/00Data organisation or data communication aspects, specifically adapted for electrophonic musical tools or instruments
    • G10H2240/121Musical libraries, i.e. musical databases indexed by musical parameters, wavetables, indexing schemes using musical parameters, musical rule bases or knowledge bases, e.g. for automatic composing methods
    • G10H2240/131Library retrieval, i.e. searching a database or selecting a specific musical piece, segment, pattern, rule or parameter set
    • G10H2240/135Library retrieval index, i.e. using an indexing scheme to efficiently retrieve a music piece
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H2240/00Data organisation or data communication aspects, specifically adapted for electrophonic musical tools or instruments
    • G10H2240/121Musical libraries, i.e. musical databases indexed by musical parameters, wavetables, indexing schemes using musical parameters, musical rule bases or knowledge bases, e.g. for automatic composing methods
    • G10H2240/131Library retrieval, i.e. searching a database or selecting a specific musical piece, segment, pattern, rule or parameter set
    • G10H2240/141Library retrieval matching, i.e. any of the steps of matching an inputted segment or phrase with musical database contents, e.g. query by humming, singing or playing; the steps may include, e.g. musical analysis of the input, musical feature extraction, query formulation, or details of the retrieval process
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10HELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H2250/00Aspects of algorithms or signal processing methods without intrinsic musical character, yet specifically adapted for or used in electrophonic musical processing
    • G10H2250/131Mathematical functions for musical analysis, processing, synthesis or composition
    • G10H2250/215Transforms, i.e. mathematical transforms into domains appropriate for musical signal processing, coding or compression
    • G10H2250/235Fourier transform; Discrete Fourier Transform [DFT]; Fast Fourier Transform [FFT]
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/20Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech

Abstract

The present invention provides an innovative technique for rapidly and accurately determining whether two audio samples match, as well as being immune to various kinds of transformations, such as playback speed variation. The relationship between the two audio samples is characterized by first matching certain fingerprint objects derived from the respective samples. A set (230) of fingerprint objects (231,232), each occurring at a particular location (242), is generated for each audio sample (210). Each location (242) is determined in dependence upon the content of the respective audio sample (210) and each fingerprint object (232) characterizes one or more local features (222) at or near the respective particular location (242). A relative value is next determined for each pair of matched fingerprint objects. A histogram of the relative values is then generated. If a statistically significant peak is found, the two audio samples can be characterized as substantially matching.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • [0001]
    This application is cross-referenced to and claims priority from PCT International Application PCT/US03/12126 filed on Apr. 18, 2003, and U.S. Provisional Application No. 60/376,055 filed on Apr. 25, 2002, which are hereby incorporated by reference.
  • FIELD OF THE INVENTION
  • [0002]
    This invention relates generally to audio signal processing over a large database of audio files. More particularly, it relates to an inventive technique for rapidly and accurately determining whether two audio samples match, as well as being immune to various transformations including playback speed variation. The inventive technique further enables accurate estimation of the transformations.
  • DESCRIPTION OF THE BACKGROUND ART
  • [0003]
    The need for fast and accurate automatic recognition of music and other audio signals continues to grow. Previously available audio recognition technology often traded off speed against accuracy, or noise immunity. In some applications, calculating a regression is necessary to estimate the slope of a time-time scatter-plot in the presence of extreme noise, which introduced a number of difficulties and lowered performance in both speed and accuracy. Previously existing audio recognition techniques were therefore incapable of performing fast and accurate recognition in the presence of significant playback speed variation, for example, in recognizing a recording that is played at a speed faster than normal.
  • [0004]
    Adding to the complexity of the problem is an increasingly popular kind of speed variation, pitch-corrected tempo variation, used by DJ's at radio stations, clubs, and elsewhere. Currently, there is no robust and reliable technique that can perform fast and accurate audio recognition in spite of the playback speed variations and/or pitch-corrected tempo variations.
  • SUMMARY OF THE INVENTION
  • [0005]
    The present invention fulfills the need in the audio recognition art by providing a fast and invariant method for characterizing the relationship between two audio files. The inventive method is accurate even in the presence of extreme noise, overcoming the aforementioned drawbacks of existing technology.
  • [0006]
    According to an aspect of the invention, the relationship between two audio samples can be characterized by first matching certain fingerprint objects derived from the respective samples. A set of fingerprint objects is generated for each audio sample. Each fingerprint object occurs at a particular location within the respective audio sample. Each location is determined in dependence upon the content of the respective audio sample and each fingerprint object characterizes one or more local features of the respective audio sample at or near the respective particular location. In one embodiment, each fingerprint object is further characterized by a variant component and an invariant component. A relative value is next determined for each pair of matched fingerprint objects. A histogram of the relative values is then generated. If a statistically significant peak is found in the histogram, then the two audio samples can be characterized as, for example, substantially matching.
  • [0007]
    According to another aspect of the invention, the above-described technique can be further enhanced by providing an estimate of a global relative value with a location of the peak on an axis of the histogram. The global relative value, in turn, can be refined by selecting a neighborhood around the peak of interest and calculating an average of the relative values in the selected neighborhood.
  • [0008]
    In yet another embodiment, in which a relative playback speed value is determined from the peak of the histogram, a compensated relative time offset value is calculated for each pair of matched fingerprint objects. Another histogram is generated based on the compensated relative time offset values. If a statistically significant peak is found in the second histogram, then the relationship between the two audio samples can be further characterized by the peak, providing further enhancement to the accuracy of the invention.
  • BRIEF DESCRIPTION OF THE FIGURES
  • [0009]
    FIG. 1 is a spectrogram representation of an analyzed audio sample.
  • [0010]
    FIG. 2 is an exemplary diagram showing fingerprint objects being generated from an audio sample in accordance with an aspect of the invention.
  • [0011]
    FIG. 3 illustrates two audio samples being compared in accordance with the principles of the present invention
  • [0012]
    FIGS. 4A-B show exemplary histograms with and without a statistically significant peak.
  • [0013]
    FIGS. 5A-B illustrate the motion of time-frequency points as the playback speed varies.
  • [0014]
    FIGS. 6A-B show corresponding times in a first audio sample (sample sound) and a second audio sample (database sound) of matching hash tokens. The slope equals one when the playback speed of the sample sound is the same as the database sound.
  • [0015]
    FIGS. 7A-D illustrate fast and efficient slope finding and histogramming techniques of the present invention.
  • DETAILED DESCRIPTION
  • [0016]
    The present invention enables fast, robust, invariant, and scalable indexing and searching over a large database of audio files and is particularly useful for audio pattern recognition applications. In some embodiments, the techniques disclosed herein improve and enhance the audio recognition system and methods disclosed in the above-referenced U.S. patent application.
  • [0017]
    A very fast and efficient comparison operation between two audio sample files is essential in building a commercially viable audio recognition system. According to an aspect of the invention, the relationship between two audio samples can be characterized by first matching certain fingerprint objects derived from a spectrogram, such as one shown in FIG. 1, of the respective audio samples. The spectrogram is a time-frequency representation/analysis that is generated by taking samples 2*K at a time in a sliding window frame and computing a Fourier Transform, thereby generating K frequency bins in each frame. The frames may overlap to improve the time resolution of the analysis. The particular parameters used depend on the kind of audio samples being processed. Preferably, discrete-time audio files with an 8 kilohertz sampling rate, frames with K=512, and a stride of 64 samples are used.
  • [0000]
    Fingerprint Objects
  • [0018]
    After a spectrogram of each audio sample is generated, it is scanned for local features, e.g., local energy peaks, as shown in FIG. 2. The matching process starts by extracting a set of fingerprint objects from the corresponding local features for each audio sample. In an exemplary embodiment, one audio sample is an unknown sound sample to be recognized and the other audio sample is a known recording stored in a database. Each fingerprint object occurs at a particular location within the respective audio sample. In some embodiments, each fingerprint object is located at some time offset within an audio file and contains a set of descriptive information about the audio file near its respective time coordinate. That is, descriptive information contained in each fingerprint object is computed with dependency on the audio sample near the respective time offset. This is encoded into a small data structure. Preferably, the location and descriptive information are determined in a way that is generally reproducible even in the presence of noise, distortion, and other transformations such as varying playback speed. In this case, each location is determined in dependence upon the content of the respective audio sample and each fingerprint object characterizes one or more local features of the respective audio sample at or near the respective particular location, e.g., location (t1,f1) or (t2,f2) as shown in FIG. 1.
  • [0019]
    In an exemplary embodiment, each fingerprint object is characterized by its location, a variant component, and an invariant component. Each local feature is a spectrogram peak and each frequency value is determined from a frequency coordinate of a corresponding spectrogram peak. The peaks are determined by searching in the vicinity of each time-frequency coordinate and selecting the points that have a greater magnitude value than its neighbors. More specifically, as shown in FIG. 2, an audio sample 210 is analyzed into a spectrogram representation 220 with regions 221 and 222 of high energy shown. Information related to local energy regions 221 and 222 is extracted and summarized into a list 230 of fingerprint objects 231, 232, etc. Each fingerprint object optionally includes a location field 242, a variant component 252, and an invariant component 262. Preferably, a neighborhood is selected such that each chosen point is the maxima within a 21×21 unit block centered around thereof. Readers are referred to the above-referenced U.S. patent application for more discussion on neighborhoods and point selection. Next, a relative value is determined for each pair of matched fingerprint objects. In some embodiments, the relative value is a quotient or difference of logarithm of parametric values of the respective audio samples. A histogram of the relative values is then generated. If a statistically significant peak is found in the histogram, then the two audio samples can be characterized as substantially matching.
  • [0020]
    Referring to FIG. 3, fingerprint object lists 310 and 320 are respectively prepared as described above for audio samples 1 and 2, respectively. Respective fingerprint objects 311 and 322 from each list are compared. Matching fingerprint objects are paired, e.g., using respective invariant components Inv and Inv′ in step 351, and put into a list in step 352. Relative values are computed for each matched pair in step 353. Next, in step 354, a histogram of relative values is generated. The histogram is searched for a statistically significant peak in step 355. If none is found in step 356, then the audio samples 1 and 2 do not match, e.g., histogram 410 of FIG. 4A. Alternatively, if a statistically significant peak is detected, then the audio samples 1 and 2 match, e.g., histogram 420 of FIG. 4B.
  • [0021]
    The above-described technique can be further enhanced by providing an estimate of a global relative value R with a location of the peak on an axis of the histogram, as illustrated in step 361. In some embodiments, R can be refined by first selecting a neighborhood around the peak of interest. In FIG. 1, this is shown as an area of interest 110 around a particular location (t1,f1). Next, an average of the relative values in the selected neighborhood is calculated. The average may be a weighted average calculated with number of points at each relative value in the selected neighborhood. In some embodiments, R can be further refined to generate a relative time offset value t′−R*t for each matched pair. Steps 362-364 show that, with these relative time offset values, a second histogram is generated, allowing a compensated time offset to be calculated.
  • [0022]
    Other kinds of time-frequency analyses may be implemented for extracting fingerprint objects, e.g., the Wigner-Ville distribution or wavelets. Also, instead of spectrogram peaks, other features, e.g., cepstral coefficients, can be used. Further, super-resolution techniques could be used to obtain finer frequency and time estimates of the time-frequency coordinates provided by the spectrogram peaks. For example, parabolic interpolation on frequency bins could be used to increase the frequency resolution. Related exemplary teachings can be found in “PARSHL: An Analysis/Synthesis Program for Non-Harmonic Sounds Based on a Sinusoidal Representation”, Julius O. Smith III and Xavier Serra, Proceedings of the International Computer Music Conference (ICMC-87, Tokyo), Computer Music Association, 1987, and in “Modern Spectral Estimation: Theory and Application,” by Steven M. Kay (January 1988) Prentice Hall, both of which are incorporated herein by reference.
  • [0000]
    Matching
  • [0023]
    In a matching operation, two audio samples are compared via their respective fingerprint objects. As discussed before with reference to FIG. 3, pairs of matched fingerprint objects are generated, each pair containing substantially matching components. One way of preparing the data to allow for fast searching is to encode the fingerprint objects into numeric tokens, such as 32-bit unsigned integers, and using the numeric tokens as a key for sorting and searching. Techniques for efficient data manipulation are well-known in the art, for example, “Art of Computer Programming, Volume 3: Sorting and Searching (2nd Edition),” by Donald Ervin Knuth (April 1998) Addison-Wesley, which is incorporated herein by reference.
  • [0024]
    In an exemplary embodiment, each fingerprint object contains an invariant component and a variant component. The invariant component refers to the ratios of frequency values corresponding to spectral peaks, as well as ratios of delta time (i.e., time difference) values between spectral peaks are invariant under time stretch. For example, referring to FIGS. 5A and 5B, if an audio sample's spectrogram has some local spectral peaks with coordinates (t1,f1), (t2,f2), and (t3,f3) then an invariant for two points is f2/f1, i.e., f2′/f1′=f2/f1. Additional invariants for 3 points are given by f3/f1, (t3−t1)/(t2−t1), or (t3−t2)/(t2−t1), or any other combination created by permuting the points and/or computing functions of these quantities or combinations of these quantities. For example, f2/f3 could be created by dividing f2/f1 by f3/f1. Furthermore, if the audio sample is linearly stretched, such as simply being played back faster, then additionally frequency and delta time enjoy a reciprocal relationship, so that quantities such as f1*(t2−t1) are also invariant. Logarithms of these quantities may be used, substituting addition and subtraction for multiplication and division. To discover both the frequency and time stretch ratios, assuming they are independent, it is necessary to have both a frequency variant and a time variant quantity.
  • [0025]
    To make matching efficient, we use the invariant part to index the fingerprints and use approximate or exact values to search. Searching using approximate matches allows for some extra robustness against distortions and rounding error, but incurs more cost if the search over the invariant components becomes a multidimensional range search. In the preferred embodiment, the invariant component of respective fingerprint objects is required to match exactly, thus yielding a system that is very fast, with a minor tradeoff against sensitivity of recognition in the presence of noise. It is important to note that this method works well even if only a minority of fingerprint objects in corresponding audio samples match correctly. In the histogram peak detection step, a peak may be statistically significant even if as few as 1-2% of the fingerprint objects are correctly matched and survive.
  • [0026]
    The variant component can also be used to narrow down the number of matching fingerprint objects, in addition to, or instead of the invariant component. For example, we could require that a variant component V from the first audio sample match a corresponding V′ from the second audio sample within +/−20%. In that case, we can form a representation of the numeric tokens such that the upper portion (e.g., most significant bits) contains the invariant components, and the lower portion (e.g., least significant bits) contains the variant components. Then, searching for an approximate match becomes a range search over the tokens composed using the lowest and highest values of the variant component. The use of an invariant component in matching is thus not strictly necessary if searching is done using a variant component. However, using an invariant component in the matching process is recommended since it helps to reduce the number of spurious matches, thus streamlining the histogramming process and reducing the amount of processing overhead.
  • [0027]
    On the other hand, the novel variant component itself may or may not be a part of the matching criteria between two fingerprint objects. The variant component represents a value that may be distorted by some simple parametric transformation going from an original recording to a sampled recording. For example, frequency variant components, such as f1, f2, f3, and time variant components such as (t2−t1), (t3−t1), or (t3−t2) may be chosen as variant components for playback speed variation. Suppose a second audio sample, say a matching rendition from a database, had a spectrogram with coordinates (t1′,f1′), (t2′,f2′), and (t3′,f3′), corresponding to the same points listed above for the first audio sample. Then the frequency component f1′ could have a scaled value f1′=Rf*f1, where Rf is a linear stretch parameter describing how much faster or slower the first sample recording was compared to the second. The variant component from each of the two matching audio samples can be used to calculate an estimate of the global stretch value, which describes a macroscopic parameter, by calculating the ratio between the two frequency values, Rf=f1′/f1. This gives the relative pitch ratio of the two matched time-frequency points; for example, Rf=2 means that the first audio sample has half the pitch (frequency) of the second. Another possibility is to use Rt=(t2′−t1′)/(t2−t1). In this case, the relative value R is the relative playback speed ratio, i.e., Rt=2 means that the first audio sample is playing back twice as fast as the second audio sample.
  • [0028]
    If Rf=1/Rt, i.e., f′/f=(t2−t1)/(t2′−t1′), then the two audio samples are related by a linear time stretch, due to the reciprocal time-frequency relationship for such audio samples. In this case, we can first use the histogramming method disclosed herein to form an estimate Rf of the relative frequency ratio using corresponding variant frequency components, and again to form an estimate of Rt of the relative playback speed, then perform a comparison to detect whether the playback relationship is linear or nonlinear.
  • [0029]
    In general, a relative value is calculated from matched fingerprint objects using corresponding variant components from the first and second audio samples. The relative value could be a simple ratio of frequencies or delta times, or some other function that results in an estimate of a global parameter used to describe the mapping between the first and second audio sample. But generally, any 2-input function F( ) may be used, e.g. R=F(v1,v1′), where v1 and v1′ are respective variant quantities. It is best if F( ) is a continuous function so that small errors in measuring v1 and v1′ result in small errors in the output R.
  • [0000]
    Histogramming
  • [0030]
    As described herein, a histogram is generated over the set of relative values calculated from the list of matching pairs of fingerprint objects. The histogram is then searched for a peak. The presence of a statistically significant peak in the histogram indicates that a possible match has occurred. This method particularly searches for a cluster in the histogram of relative values instead of differences of time offsets, such as (t1′−t1). According to a principle of the present invention, a histogram serves to form bins of count values, each bin corresponding to a particular value along the independent axis of the histogram. For the purpose of this invention, generating a histogram may be accomplished by simply sorting the list of relative values. Therefore, a fast and efficient way of detecting the peak of a histogram of a list of values is to sort the list in ascending order, then scan for the largest clump of items having the same or similar values.
  • [0000]
    Statistical Significance
  • [0031]
    As discussed herein, with the present invention, two audio samples can be correctly matched even if only as few as 2% of the fingerprint objects survive all the distortions and are correctly matched. This is possible by scoring the comparison between the two audio samples. Specifically, a neighborhood is chosen around the peak of the histogram and all the matching pairs falling into the neighborhood are counted, giving the score. Additionally, a weighted score may be computed, discounting the contribution of pairs that are farther from the center of the peak.
  • [0032]
    One way to estimate the cutoff criterion is to assume that the probability distribution of the score of a non-matching track falls off with an exponential tail. The model is applied to the actual measured distribution of scores of non-matching tracks. Next the cumulative probability distribution of the highest score over a database of N tracks (e.g., taken as the Nth power of the cumulative probability distribution of a single non-matching score) is calculated. Once the probability curve is known and a maximum level of false positives is chosen (e.g., 0.5%), then a numeric threshold can be chosen and used to determine whether the histogram peak has a statistically significant number of matching pairs.
  • [0000]
    Hyperfine Estimation
  • [0033]
    Once a statistically significant histogram peak is found, a high-resolution “hyperfine” estimate of the global relative value (such as relative playback speed) may be computed. This is accomplished by choosing a neighborhood around the peak, e.g., including an interval about 3 or 5 bins wide centered on the peak histogram bin, and calculating an average of the relative values in the neighborhood. Using this technique, we can find relative playback speed accurate to within 0.05%. With offset derivation disclosed herein, the global time offset may be estimated with better than 1 millisecond accuracy, which is finer than the time resolution of the spectrogram frames discussed above.
  • [0000]
    Robust Regression
  • [0034]
    As discussed in the above-referenced U.S. patent application, in the case that the samples actually matched, a diagonal line could be seen in a scatterplot where matching samples have the corresponding time coordinates (t′,t) of matching fingerprint objects plotted against each other, as shown in FIG. 6A. The challenge is to find the equation of the regressor, which is determined by the slope and offset of the line, in the presence of a high amount of noise. The slope indicates the relative playback speed, and the offset is the relative offset from the beginning of one audio sample to the beginning of the second. Conventional regression techniques, such as least-mean square fitting, are available, for example, “Numerical Recipes in C: The Art of Scientific Computing (2nd Edition),” by William H. Press, Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling (January 1993), Cambridge University Press, which is incorporated herein by reference. Unfortunately, these conventional techniques suffer from disproportionate sensitivity, wherein a single far outlier can drastically skew the estimated regression parameters. In practice, points are often dominated by outliers, making it very difficult to detect the correct diagonal line. Other techniques for robust regression can be used to overcome the outlier problem to find a linear relation among points in the presence of noise, but these tend to be slow and iterative and have the possibility of getting stuck in a local optimum. A wide variety of techniques exist in the literature for finding an unknown linear regressor. The Matlab toolkit, available from The Mathworks and incorporated herein by reference, contains a variety of software routines for regression analysis.
  • [0035]
    The present invention provides an inventive method of estimating the relative playback speed (or, equivalently, the reciprocal of the relative pitch, in the case of a linear playback relationship) that solves the problem of finding a regression line in the time-time scatterplot even if the slope of the match does not equal to one, e.g., FIG. 6B. The use of the histogram of local relative playback speeds, as disclosed herein, takes advantage of information not previously considered and provides an unexpected advantage of quickly and efficiently solving the regression problem.
  • [0036]
    To find the offset, assume that the corresponding time points have the relation
    offset=t1′−R t *t1,
    where Rt is obtained as discussed before. This is the compensated time offset and serves to normalize the time coordinate systems between the two audio samples. This can also be seen as a shear transformation on the time-time scatterplot that makes the diagonal line of unknown slope in FIG. 7A vertical in FIG. 7C. Histogram 720 of FIG. 7B illustrates a peak of accumulated relative playback speed ratios indicating the global relative playback speed ratio R. New relative values are then given by the offset formula, and a new histogram 740 is generated, as seen in FIG. 7D. The peak of the new histogram 740 gives an estimate of the global offset, which can be sharpened by using an average of the values in the peak's neighborhood, as described above.
  • [0037]
    In summary, the first histogramming stage provides a way to estimate the relative playback speed, as well determining whether a match exists. The second histogramming stage ensures that the candidate matching audio samples have a significant number of fingerprint objects that are also temporally aligned. The second histogramming stage also serves as a second independent screening criterion and helps to lower the probability of false positives, thus providing a stronger criterion for deciding whether two audio samples match. The second histogramming stage may be optionally performed only if there is a statistically significant peak in the first histogram, thus saving computational resource and effort. A further optimization may be optionally performed, e.g., to reduce computational clutter, instead of computing the second histogram over all the pairs of matched fingerprint objects in the list, the second histogram can be generated using only the matching pairs corresponding to the first histogram peak.
  • [0000]
    Synchronization of Multiple Recordings
  • [0038]
    The present invention may be implemented for cueing and time alignment of unsynchronized audio recordings. For example, suppose a DAT recorder and a cassette recorder were operated independently with different microphones at slightly different locations or environments. If it is later desired to combine the two recordings from respective recorders into one mix, the two tracks may be synchronized using the robust regression technique described herein to obtain the time offset. As such, even if the unsynchronized recorders operate at slightly different speeds, the relative speed can be determined with a high degree of accuracy, allowing one recording be compensated with reference to another. This is especially useful if it is found that one of the recordings has become corrupted and needs to be supplemented from another source. The time alignment and synchronization as described herein thus allow for transparent mixing.
  • [0000]
    Database Search
  • [0039]
    Since the comparison method is extremely fast, it is possible to pre-process a large database of audio samples into respective lists of fingerprint objects. As one skilled in the art would appreciate, an unknown audio sample may therefore be pre-processed into its own respective list of fingerprint objects using currently available data processing techniques. The above described matching, histogramming, and peak detection techniques can then be carried out using the pre-processed fingerprint objects in the database to find a match.
  • [0040]
    Although the present invention and its advantages have been described in detail, it should be understood that the present invention is not limited to or defined by what is shown or discussed herein. In particular, drawings and description disclosed herein illustrate technologies related to the invention, show examples of the invention, and provide examples of using the invention and are not to be construed as limiting the present invention. Known methods, techniques, or systems may be discussed without giving details, so to avoid obscuring the principles of the invention. As it will be appreciated by one of ordinary skill in the art, the present invention can be implemented, modified, or otherwise altered without departing from the principles and spirit of the present invention. For example, methods, techniques, and steps described herein can be implemented or otherwise realized in a form of computer-executable instructions embodied in a computer readable medium. Alternatively, the present invention can be implemented in a computer system having a client and a server. The client sends information, e.g., fingerprint objects, necessary for the characterization of the relationship between the first and second audio samples to the server where the characterization is performed. Accordingly, the scope of the invention should be determined by the following claims and their legal equivalents.

Claims (18)

1. A method of characterizing a relationship between a first and a second audio samples, comprising the steps of:
generating a first set of fingerprint objects for the first audio sample, each fingerprint object occurring at a respective location within the first audio sample, the respective location being determined in dependence upon the content of the first audio sample, and each fingerprint object characterising one or more features of the first audio sample at or near each respective location;
generating a second set of fingerprint objects for the second audio sample, each fingerprint object occurring at a respective location within the second audio sample, the respective location being determined in dependence upon the content of the second audio sample, and each fingerprint object characterising one or more features of the second audio sample at or near each respective location;
pairing fingerprint objects by matching a first fingerprint object from the first audio sample with a second fingerprint object from the second audio sample that is substantially similar to the first fingerprint object;
generating, based on the pairing step, a list of pairs of matched fingerprint objects;
determining a relative value for each pair of matched fingerprint objects;
generating a histogram of the relative values; and
searching for a statistically significant peak in the histogram, the peak characterizing the relationship between the first and second audio samples.
2. The method according to claim 1 in which the relationship between the first and second audio samples is characterized as substantially matching if a statistically significant peak is found.
3. The method according to claim 1 or 2, further comprising the step of estimating a global relative value with a location of the peak on an axis of the histogram, the global relative value further characterizing the relationship between the first and second audio samples.
4. The method according to claim 3, further comprising the step of determining a hyperfine estimate of the global relative value, wherein the step of determining comprises:
selecting a neighbourhood around the peak, and
calculating an average of the relative values in the neighbourhood.
5. The method according to claim 1 in which each fingerprint object has an invariant component, and the first and second fingerprint objects in each pair of matched fingerprint objects have matching invariant components.
6. The method according to claim 5 in which the invariant component is generated using at least one of:
(i) a ratio between a first and a second frequency values, each frequency value being respectively determined from a first and a second local features near the respective location of each fingerprint object;
(ii) a product between a frequency value and a delta time value, the frequency value being determined from a first local feature, and the delta time value being determined between the first local feature and a second local feature near the respective location of each fingerprint object; and
(iii) a ratio between a first and a second delta time values, the first delta time value being determined from a first and a second local features, the second delta time value being determined from the first and a third local features, each local feature being near the respective location of each fingerprint object.
7. The method according to claim 6 in which each local feature is a spectrogram peak and each frequency value is determined from a frequency coordinate of a corresponding spectrogram peak.
8. The method according to claim 1 or 5 in which each fingerprint object has a variant component, and the relative value of each pair of matched fingerprint objects is determined using respective variant components of the first and second fingerprint objects.
9. The method according to claim 8 in which the variant component is a frequency value determined from a local feature near the respective location of each fingerprint object such that the relative value of a pair of matched fingerprint objects being characterized as a ratio of respective frequency values of the first and second fingerprint objects and the peak in the histogram characterizing the relationship between the first and second audio samples being characterized as a relative pitch, or, in case of linear stretch, a relative playback speed.
10. The method according to claim 9, wherein the ratio of respective frequency values is characterized as being either a division or a difference of logarithms.
11. The method according to claim 9, in which each local feature is a spectrogram peak and each frequency value is determined from a frequency coordinate of a corresponding spectrogram peak.
12. The method according to claim 8, in which the variant component is a delta time value determined from a first and a second local features near the respective location of each fingerprint object such that the relative value of a pair of matched fingerprint objects being characterized as a ratio of respective variant delta time values and the peak in the histogram characterizing the relationship between the first and second audio samples being characterized as a relative playback speed, or, in case of linear stretch, a relative pitch.
13. The method according to claim 12, wherein the ratio of respective variant delta time values is characterized as being either a division or a difference of logarithms.
14. The method according to claim 12, in which each local feature is a spectrogram peak and each frequency value is determined from a frequency coordinate of a corresponding spectrogram peak.
15. The method according to claim 8, further comprising the steps of:
determining a relative pitch for the first and second audio samples using the respective variant components, wherein each variant component is a frequency value determined from a local feature near the respective location of each fingerprint object;
determining a relative playback speed for the first and second audio samples using the respective variant components, wherein each variant component is a delta time value determined from a first and a second local features near the respective location of each fingerprint object; and
detecting if the relative pitch and a reciprocal of the relative playback speed are substantially different, in which case the relationship between the first and second audio samples is characterized as nonlinear.
16. The method according to claim 1, wherein R is a relative playback speed value determined from the peak of the histogram of the relative values, further comprising the steps of:
for each pair of matched fingerprint objects in the list, determining a compensated relative time offset value, t−R*t′, where t and t′ are locations in time with respect to the first and second fingerprint objects;
generating a second histogram of the compensated relative time offset values; and
searching for a statistically significant peak in the second histogram of the compensated relative time offset values, the peak further characterizing the relationship between the first and second audio samples.
17. A computer program product for performing a method according to any preceding claim.
18. A computer system for performing a method according to any one of claims 1 to 16, the computer system comprising a client for sending information necessary for the characterization of the relationship between the first and second audio samples to a server that performs the characterization.
US10978313 2002-04-25 2004-10-21 Robust and invariant audio pattern matching Active 2025-03-31 US7627477B2 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
US37605502 true 2002-04-25 2002-04-25
WOPCT/US03/12126 2003-04-18
PCT/US2003/012126 WO2003091990A1 (en) 2002-04-25 2003-04-18 Robust and invariant audio pattern matching
US10978313 US7627477B2 (en) 2002-04-25 2004-10-21 Robust and invariant audio pattern matching

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US10978313 US7627477B2 (en) 2002-04-25 2004-10-21 Robust and invariant audio pattern matching

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2003/012126 Continuation WO2003091990A1 (en) 2002-04-25 2003-04-18 Robust and invariant audio pattern matching

Publications (3)

Publication Number Publication Date
US20050177372A1 true US20050177372A1 (en) 2005-08-11
US20090265174A9 true true US20090265174A9 (en) 2009-10-22
US7627477B2 US7627477B2 (en) 2009-12-01

Family

ID=29270756

Family Applications (1)

Application Number Title Priority Date Filing Date
US10978313 Active 2025-03-31 US7627477B2 (en) 2002-04-25 2004-10-21 Robust and invariant audio pattern matching

Country Status (10)

Country Link
US (1) US7627477B2 (en)
JP (1) JP4425126B2 (en)
KR (1) KR100820385B1 (en)
CN (1) CN1315110C (en)
CA (1) CA2483104C (en)
DE (1) DE60323086D1 (en)
DK (1) DK1504445T3 (en)
EP (1) EP1504445B1 (en)
ES (1) ES2312772T3 (en)
WO (1) WO2003091990A1 (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090012638A1 (en) * 2007-07-06 2009-01-08 Xia Lou Feature extraction for identification and classification of audio signals
US20130255473A1 (en) * 2012-03-29 2013-10-03 Sony Corporation Tonal component detection method, tonal component detection apparatus, and program
US8831763B1 (en) * 2011-10-18 2014-09-09 Google Inc. Intelligent interest point pruning for audio matching
US9069849B1 (en) * 2012-10-10 2015-06-30 Google Inc. Methods for enforcing time alignment for speed resistant audio matching
US9129015B1 (en) * 2012-06-26 2015-09-08 Google Inc. Min/max filter for audio matching
US9390719B1 (en) * 2012-10-09 2016-07-12 Google Inc. Interest points density control for audio matching

Families Citing this family (168)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6834308B1 (en) 2000-02-17 2004-12-21 Audible Magic Corporation Method and apparatus for identifying media content presented on a media playing device
US6990453B2 (en) 2000-07-31 2006-01-24 Landmark Digital Services Llc System and methods for recognizing sound and music signals in high noise and distortion
US7853664B1 (en) * 2000-07-31 2010-12-14 Landmark Digital Services Llc Method and system for purchasing pre-recorded music
US7562012B1 (en) 2000-11-03 2009-07-14 Audible Magic Corporation Method and apparatus for creating a unique audio signature
WO2002082271A1 (en) 2001-04-05 2002-10-17 Audible Magic Corporation Copyright detection and protection system and method
US8972481B2 (en) 2001-07-20 2015-03-03 Audible Magic, Inc. Playlist generation method and apparatus
US7877438B2 (en) 2001-07-20 2011-01-25 Audible Magic Corporation Method and apparatus for identifying new media content
US7239981B2 (en) 2002-07-26 2007-07-03 Arbitron Inc. Systems and methods for gathering audience measurement data
US9711153B2 (en) 2002-09-27 2017-07-18 The Nielsen Company (Us), Llc Activating functions in processing devices using encoded audio and detecting audio signatures
US8959016B2 (en) 2002-09-27 2015-02-17 The Nielsen Company (Us), Llc Activating functions in processing devices using start codes embedded in audio
CN1745374A (en) 2002-12-27 2006-03-08 尼尔逊媒介研究股份有限公司 Methods and apparatus for transcoding metadata
US8332326B2 (en) 2003-02-01 2012-12-11 Audible Magic Corporation Method and apparatus to identify a work received by a processing system
US8020000B2 (en) 2003-07-11 2011-09-13 Gracenote, Inc. Method and device for generating and detecting a fingerprint functioning as a trigger marker in a multimedia signal
EP1652385B1 (en) * 2003-07-25 2007-09-12 Philips Electronics N.V. Method and device for generating and detecting fingerprints for synchronizing audio and video
US9053181B2 (en) 2003-11-03 2015-06-09 James W. Wieder Adaptive personalized playback or presentation using count
US9053299B2 (en) 2003-11-03 2015-06-09 James W. Wieder Adaptive personalized playback or presentation using rating
US7884274B1 (en) 2003-11-03 2011-02-08 Wieder James W Adaptive personalized music and entertainment
US8396800B1 (en) 2003-11-03 2013-03-12 James W. Wieder Adaptive personalized music and entertainment
US20150128039A1 (en) 2003-11-03 2015-05-07 James W. Wieder Newness Control of a Personalized Music and/or Entertainment Sequence
US9098681B2 (en) 2003-11-03 2015-08-04 James W. Wieder Adaptive personalized playback or presentation using cumulative time
US8001612B1 (en) 2003-11-03 2011-08-16 Wieder James W Distributing digital-works and usage-rights to user-devices
US8554681B1 (en) * 2003-11-03 2013-10-08 James W. Wieder Providing “identified” compositions and digital-works
DE60319449D1 (en) * 2003-11-27 2008-04-10 Advestigo Interception of multimedia documents
WO2005079499A3 (en) 2004-02-19 2006-05-11 Landmark Digital Llc Method and apparatus for identification of broadcast source
CN101142591A (en) 2004-04-19 2008-03-12 兰德马克数字服务有限责任公司 Content sampling and identification
US20050267750A1 (en) 2004-05-27 2005-12-01 Anonymous Media, Llc Media usage monitoring and measurement system and method
US7739062B2 (en) * 2004-06-24 2010-06-15 Landmark Digital Services Llc Method of characterizing the overlap of two media segments
US8130746B2 (en) 2004-07-28 2012-03-06 Audible Magic Corporation System for distributing decoy content in a peer to peer network
US20070016918A1 (en) * 2005-05-20 2007-01-18 Alcorn Allan E Detecting and tracking advertisements
US7623823B2 (en) 2004-08-31 2009-11-24 Integrated Media Measurement, Inc. Detecting and measuring exposure to media content items
DE102004046746B4 (en) 2004-09-27 2007-03-01 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. A method for synchronizing additional data and basic data
CA2595634C (en) 2005-02-08 2014-12-30 Landmark Digital Services Llc Automatic identification of repeated material in audio signals
DE102005014477A1 (en) 2005-03-30 2006-10-12 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Apparatus and method for generating a data stream and for generating a multi-channel representation
US7529659B2 (en) 2005-09-28 2009-05-05 Audible Magic Corporation Method and apparatus for identifying an unknown work
US9646005B2 (en) 2005-10-26 2017-05-09 Cortica, Ltd. System and method for creating a database of multimedia content elements assigned to users
US9235557B2 (en) 2005-10-26 2016-01-12 Cortica, Ltd. System and method thereof for dynamically associating a link to an information resource with a multimedia content displayed in a web-page
US9031999B2 (en) 2005-10-26 2015-05-12 Cortica, Ltd. System and methods for generation of a concept based database
US9558449B2 (en) 2005-10-26 2017-01-31 Cortica, Ltd. System and method for identifying a target area in a multimedia content element
US8818916B2 (en) * 2005-10-26 2014-08-26 Cortica, Ltd. System and method for linking multimedia data elements to web pages
US8655801B2 (en) 2005-10-26 2014-02-18 Cortica, Ltd. Computing device, a system and a method for parallel processing of data streams
US9529984B2 (en) 2005-10-26 2016-12-27 Cortica, Ltd. System and method for verification of user identification based on multimedia content elements
US9286623B2 (en) 2005-10-26 2016-03-15 Cortica, Ltd. Method for determining an area within a multimedia content element over which an advertisement can be displayed
US8312031B2 (en) 2005-10-26 2012-11-13 Cortica Ltd. System and method for generation of complex signatures for multimedia data content
US9477658B2 (en) 2005-10-26 2016-10-25 Cortica, Ltd. Systems and method for speech to speech translation using cores of a natural liquid architecture system
US9256668B2 (en) 2005-10-26 2016-02-09 Cortica, Ltd. System and method of detecting common patterns within unstructured data elements retrieved from big data sources
US8386400B2 (en) 2005-10-26 2013-02-26 Cortica Ltd. Unsupervised clustering of multimedia data using a large-scale matching system
US8112376B2 (en) 2005-10-26 2012-02-07 Cortica Ltd. Signature based system and methods for generation of personalized multimedia channels
US8266185B2 (en) 2005-10-26 2012-09-11 Cortica Ltd. System and methods thereof for generation of searchable structures respective of multimedia data content
US9372940B2 (en) 2005-10-26 2016-06-21 Cortica, Ltd. Apparatus and method for determining user attention using a deep-content-classification (DCC) system
US9489431B2 (en) 2005-10-26 2016-11-08 Cortica, Ltd. System and method for distributed search-by-content
US9466068B2 (en) 2005-10-26 2016-10-11 Cortica, Ltd. System and method for determining a pupillary response to a multimedia data element
US9218606B2 (en) 2005-10-26 2015-12-22 Cortica, Ltd. System and method for brand monitoring and trend analysis based on deep-content-classification
US9767143B2 (en) 2005-10-26 2017-09-19 Cortica, Ltd. System and method for caching of concept structures
US9396435B2 (en) 2005-10-26 2016-07-19 Cortica, Ltd. System and method for identification of deviations from periodic behavior patterns in multimedia content
US9384196B2 (en) 2005-10-26 2016-07-05 Cortica, Ltd. Signature generation for multimedia deep-content-classification by a large-scale matching system and method thereof
US9191626B2 (en) 2005-10-26 2015-11-17 Cortica, Ltd. System and methods thereof for visual analysis of an image on a web-page and matching an advertisement thereto
US9747420B2 (en) 2005-10-26 2017-08-29 Cortica, Ltd. System and method for diagnosing a patient based on an analysis of multimedia content
US8326775B2 (en) * 2005-10-26 2012-12-04 Cortica Ltd. Signature generation for multimedia deep-content-classification by a large-scale matching system and method thereof
US9639532B2 (en) 2005-10-26 2017-05-02 Cortica, Ltd. Context-based analysis of multimedia content items using signatures of multimedia elements and matching concepts
US9087049B2 (en) 2005-10-26 2015-07-21 Cortica, Ltd. System and method for context translation of natural language
US9330189B2 (en) 2005-10-26 2016-05-03 Cortica, Ltd. System and method for capturing a multimedia content item by a mobile device and matching sequentially relevant content to the multimedia content item
US7688686B2 (en) 2005-10-27 2010-03-30 Microsoft Corporation Enhanced table of contents (TOC) identifiers
GB2431839B (en) 2005-10-28 2010-05-19 Sony Uk Ltd Audio processing
KR100803206B1 (en) 2005-11-11 2008-02-14 삼성전자주식회사 Apparatus and method for generating audio fingerprint and searching audio data
WO2008042953A1 (en) 2006-10-03 2008-04-10 Shazam Entertainment, Ltd. Method for high throughput of identification of distributed broadcast content
KR101266267B1 (en) 2006-10-05 2013-05-23 스플렁크 인코퍼레이티드 Time Series Search Engine
US8077839B2 (en) * 2007-01-09 2011-12-13 Freescale Semiconductor, Inc. Handheld device for dialing of phone numbers extracted from a voicemail
US20080317226A1 (en) * 2007-01-09 2008-12-25 Freescale Semiconductor, Inc. Handheld device for transmitting a visual format message
US8849432B2 (en) * 2007-05-31 2014-09-30 Adobe Systems Incorporated Acoustic pattern identification using spectral characteristics to synchronize audio and/or video
US8006314B2 (en) 2007-07-27 2011-08-23 Audible Magic Corporation System for identifying content of digital data
US8213521B2 (en) 2007-08-15 2012-07-03 The Nielsen Company (Us), Llc Methods and apparatus for audience measurement using global signature representation and matching
GB2463836B (en) * 2007-08-21 2012-10-10 Cortica Ltd Signature generation for multimedia deep-content-classification by a large-scale matching system and method thereof
US8473283B2 (en) * 2007-11-02 2013-06-25 Soundhound, Inc. Pitch selection modules in a system for automatic transcription of sung or hummed melodies
CN101226741B (en) 2007-12-28 2011-06-15 无敌科技(西安)有限公司 Method for detecting movable voice endpoint
DE102008009024A1 (en) * 2008-02-14 2009-08-27 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Apparatus and method for synchronizing multi-channel extension data with an audio signal and for processing the audio signal
DE102008009025A1 (en) * 2008-02-14 2009-08-27 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Apparatus and method for computing a fingerprint of an audio signal, apparatus and methods for synchronizing and Apparatus and method for characterizing a test audio signal
GB2457694B (en) 2008-02-21 2012-09-26 Snell Ltd Method of Deriving an Audio-Visual Signature
CA2897271C (en) 2008-03-10 2017-11-28 Sascha Disch Device and method for manipulating an audio signal having a transient event
GB0804983D0 (en) * 2008-03-17 2008-04-16 Taylor Nelson Sofres Plc Digital signature generating device and method
EP2114079B2 (en) * 2008-05-02 2018-01-24 Psytechnics Ltd Method and apparatus for aligning signals
JP2010033265A (en) 2008-07-28 2010-02-12 Nec Corp Method and system for distributing content
US9667365B2 (en) 2008-10-24 2017-05-30 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US8121830B2 (en) 2008-10-24 2012-02-21 The Nielsen Company (Us), Llc Methods and apparatus to extract data encoded in media content
US8359205B2 (en) 2008-10-24 2013-01-22 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US8508357B2 (en) 2008-11-26 2013-08-13 The Nielsen Company (Us), Llc Methods and apparatus to encode and decode audio for shopper location and advertisement presentation tracking
US8199651B1 (en) 2009-03-16 2012-06-12 Audible Magic Corporation Method and system for modifying communication flows at a port level
WO2010106734A1 (en) * 2009-03-18 2010-09-23 日本電気株式会社 Audio signal processing device
US8351712B2 (en) 2009-04-27 2013-01-08 The Neilsen Company (US), LLC Methods and apparatus to perform image classification based on pseudorandom features
JP2012525655A (en) 2009-05-01 2012-10-22 ザ ニールセン カンパニー (ユー エス) エルエルシー Method for providing secondary content associated with the primary broadcast media content, devices, and articles of manufacture
GB0908153D0 (en) * 2009-05-12 2009-06-24 Nokia Corp An apparatus
US8687839B2 (en) 2009-05-21 2014-04-01 Digimarc Corporation Robust signatures derived from local nonlinear filters
US8718805B2 (en) * 2009-05-27 2014-05-06 Spot411 Technologies, Inc. Audio-based synchronization to media
US8489774B2 (en) 2009-05-27 2013-07-16 Spot411 Technologies, Inc. Synchronized delivery of interactive content
US8190663B2 (en) * 2009-07-06 2012-05-29 Osterreichisches Forschungsinstitut Fur Artificial Intelligence Der Osterreichischen Studiengesellschaft Fur Kybernetik Of Freyung Method and a system for identifying similar audio tracks
US20120237041A1 (en) 2009-07-24 2012-09-20 Johannes Kepler Universität Linz Method And An Apparatus For Deriving Information From An Audio Track And Determining Similarity Between Audio Tracks
US20110041154A1 (en) * 2009-08-14 2011-02-17 All Media Guide, Llc Content Recognition and Synchronization on a Television or Consumer Electronics Device
US8161071B2 (en) 2009-09-30 2012-04-17 United Video Properties, Inc. Systems and methods for audio asset storage and management
US8677400B2 (en) 2009-09-30 2014-03-18 United Video Properties, Inc. Systems and methods for identifying audio content using an interactive media guidance application
US8706276B2 (en) 2009-10-09 2014-04-22 The Trustees Of Columbia University In The City Of New York Systems, methods, and media for identifying matching audio
US8521779B2 (en) 2009-10-09 2013-08-27 Adelphoi Limited Metadata record generation
US8121618B2 (en) 2009-10-28 2012-02-21 Digimarc Corporation Intuitive computing methods and systems
US8860883B2 (en) * 2009-11-30 2014-10-14 Miranda Technologies Partnership Method and apparatus for providing signatures of audio/video signals and for making use thereof
US8682145B2 (en) 2009-12-04 2014-03-25 Tivo Inc. Recording system based on multimedia content fingerprints
US9197736B2 (en) * 2009-12-31 2015-11-24 Digimarc Corporation Intuitive computing methods and systems
US8886531B2 (en) * 2010-01-13 2014-11-11 Rovi Technologies Corporation Apparatus and method for generating an audio fingerprint and using a two-stage query
CN102959544B (en) 2010-05-04 2016-06-08 沙扎姆娱乐有限公司 A method and system for synchronizing media
WO2012112573A1 (en) 2011-02-18 2012-08-23 Shazam Entertainment Ltd. Methods and systems for identifying content in a data stream by a client device
KR20150095957A (en) 2010-05-04 2015-08-21 샤잠 엔터테인먼트 리미티드 Methods and systems for processing a sample of media stream
US9159338B2 (en) 2010-05-04 2015-10-13 Shazam Entertainment Ltd. Systems and methods of rendering a textual animation
JP5907511B2 (en) 2010-06-09 2016-04-26 アデルフォイ リミテッド System and method for audio media recognition
US9876905B2 (en) 2010-09-29 2018-01-23 Genesys Telecommunications Laboratories, Inc. System for initiating interactive communication in response to audio codes
US20140046967A1 (en) * 2010-11-22 2014-02-13 Listening Methods, Llc System and method for pattern recognition and analysis
US8589171B2 (en) 2011-03-17 2013-11-19 Remote Media, Llc System and method for custom marking a media file for file matching
US8688631B2 (en) 2011-03-17 2014-04-01 Alexander Savenok System and method for media file synchronization
US8478719B2 (en) 2011-03-17 2013-07-02 Remote Media LLC System and method for media file synchronization
US9380356B2 (en) 2011-04-12 2016-06-28 The Nielsen Company (Us), Llc Methods and apparatus to generate a tag for media content
US8996557B2 (en) 2011-05-18 2015-03-31 Microsoft Technology Licensing, Llc Query and matching for content recognition
US9286909B2 (en) 2011-06-06 2016-03-15 Bridge Mediatech, S.L. Method and system for robust audio hashing
CA2837741A1 (en) 2011-06-08 2012-12-13 Shazam Entertainment Ltd. Methods and systems for performing comparisons of received data and providing a follow-on service based on the comparisons
KR101578279B1 (en) 2011-06-10 2015-12-28 샤잠 엔터테인먼트 리미티드 Methods and systems for identifying content in a data stream
US9210208B2 (en) 2011-06-21 2015-12-08 The Nielsen Company (Us), Llc Monitoring streaming media content
US8620646B2 (en) * 2011-08-08 2013-12-31 The Intellisis Corporation System and method for tracking sound pitch across an audio signal using harmonic envelope
US9461759B2 (en) 2011-08-30 2016-10-04 Iheartmedia Management Services, Inc. Identification of changed broadcast media items
US8639178B2 (en) 2011-08-30 2014-01-28 Clear Channel Management Sevices, Inc. Broadcast source identification based on matching broadcast signal fingerprints
US9374183B2 (en) 2011-08-30 2016-06-21 Iheartmedia Management Services, Inc. Broadcast source identification based on matching via bit count
US9049496B2 (en) * 2011-09-01 2015-06-02 Gracenote, Inc. Media source identification
US9113202B1 (en) * 2011-09-21 2015-08-18 Google Inc. Inverted client-side fingerprinting and matching
US9460465B2 (en) 2011-09-21 2016-10-04 Genesys Telecommunications Laboratories, Inc. Graphical menu builder for encoding applications in an image
US9384272B2 (en) 2011-10-05 2016-07-05 The Trustees Of Columbia University In The City Of New York Methods, systems, and media for identifying similar songs using jumpcodes
US8977194B2 (en) 2011-12-16 2015-03-10 The Nielsen Company (Us), Llc Media exposure and verification utilizing inductive coupling
US8538333B2 (en) 2011-12-16 2013-09-17 Arbitron Inc. Media exposure linking utilizing bluetooth signal characteristics
US9268845B1 (en) * 2012-03-08 2016-02-23 Google Inc. Audio matching using time alignment, frequency alignment, and interest point overlap to filter false positives
EP2648418A1 (en) * 2012-04-05 2013-10-09 Thomson Licensing Synchronization of multimedia streams
US9209978B2 (en) 2012-05-15 2015-12-08 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
US9235867B2 (en) * 2012-06-04 2016-01-12 Microsoft Technology Licensing, Llc Concurrent media delivery
US9282366B2 (en) 2012-08-13 2016-03-08 The Nielsen Company (Us), Llc Methods and apparatus to communicate audience measurement information
US20140074466A1 (en) * 2012-09-10 2014-03-13 Google Inc. Answering questions using environmental context
US9081778B2 (en) 2012-09-25 2015-07-14 Audible Magic Corporation Using digital fingerprints to associate data with a work
EP2731030A1 (en) * 2012-11-13 2014-05-14 Samsung Electronics Co., Ltd Music information searching method and apparatus thereof
US9195649B2 (en) 2012-12-21 2015-11-24 The Nielsen Company (Us), Llc Audio processing techniques for semantic audio recognition and report generation
US9183849B2 (en) 2012-12-21 2015-11-10 The Nielsen Company (Us), Llc Audio matching with semantic audio recognition and report generation
US9158760B2 (en) 2012-12-21 2015-10-13 The Nielsen Company (Us), Llc Audio decoding with supplemental semantic audio recognition and report generation
CN103971689B (en) * 2013-02-04 2016-01-27 腾讯科技(深圳)有限公司 An audio recognition method and apparatus
US9313544B2 (en) 2013-02-14 2016-04-12 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
FR3002713B1 (en) * 2013-02-27 2015-02-27 Inst Mines Telecom Generation of a signature of a musical audio signal
US9451048B2 (en) 2013-03-12 2016-09-20 Shazam Investments Ltd. Methods and systems for identifying information of a broadcast station and information of broadcasted content
US20140278845A1 (en) 2013-03-15 2014-09-18 Shazam Investments Limited Methods and Systems for Identifying Target Media Content and Determining Supplemental Information about the Target Media Content
US9773058B2 (en) 2013-03-15 2017-09-26 Shazam Investments Ltd. Methods and systems for arranging and searching a database of media content recordings
US9390170B2 (en) 2013-03-15 2016-07-12 Shazam Investments Ltd. Methods and systems for arranging and searching a database of media content recordings
WO2014169238A1 (en) 2013-04-11 2014-10-16 Digimarc Corporation Methods for object recognition and related arrangements
US9460201B2 (en) 2013-05-06 2016-10-04 Iheartmedia Management Services, Inc. Unordered matching of audio fingerprints
CN103402118B (en) * 2013-07-05 2017-12-01 Tcl集团股份有限公司 A media program interaction method and system
US20150039321A1 (en) 2013-07-31 2015-02-05 Arbitron Inc. Apparatus, System and Method for Reading Codes From Digital Audio on a Processing Device
US9711152B2 (en) 2013-07-31 2017-07-18 The Nielsen Company (Us), Llc Systems apparatus and methods for encoding/decoding persistent universal media codes to encoded audio
US9275427B1 (en) * 2013-09-05 2016-03-01 Google Inc. Multi-channel audio video fingerprinting
US9053711B1 (en) 2013-09-10 2015-06-09 Ampersand, Inc. Method of matching a digitized stream of audio signals to a known audio recording
CN104637496A (en) * 2013-11-11 2015-05-20 财团法人资讯工业策进会 Computer system and audio matching method
NL2011893C (en) * 2013-12-04 2015-06-08 Stichting Incas3 Method and system for predicting human activity.
US9426525B2 (en) 2013-12-31 2016-08-23 The Nielsen Company (Us), Llc. Methods and apparatus to count people in an audience
WO2015118431A1 (en) 2014-02-05 2015-08-13 Edge Innovation, Lda. Method for capture and analysis of multimedia content
US9699499B2 (en) 2014-04-30 2017-07-04 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
EP3023884A1 (en) * 2014-11-21 2016-05-25 Thomson Licensing Method and apparatus for generating fingerprint of an audio signal
WO2016086905A1 (en) * 2014-12-05 2016-06-09 Monitoreo Tecnológico, S.A Method for measuring audiences
CN106294331A (en) * 2015-05-11 2017-01-04 阿里巴巴集团控股有限公司 Audio information retrieval method and device
US9762965B2 (en) 2015-05-29 2017-09-12 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
US9596502B1 (en) 2015-12-21 2017-03-14 Max Abecassis Integration of multiple synchronization methodologies
US9516373B1 (en) 2015-12-21 2016-12-06 Max Abecassis Presets of synchronized second screen functions
US9786298B1 (en) 2016-04-08 2017-10-10 Source Digital, Inc. Audio fingerprinting based on audio energy characteristics

Citations (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4415767A (en) * 1981-10-19 1983-11-15 Votan Method and apparatus for speech recognition and reproduction
US4450531A (en) * 1982-09-10 1984-05-22 Ensco, Inc. Broadcast signal recognition system and method
US4843562A (en) * 1987-06-24 1989-06-27 Broadcast Data Systems Limited Partnership Broadcast information classification system and method
US5210820A (en) * 1990-05-02 1993-05-11 Broadcast Data Systems Limited Partnership Signal recognition system and method
US5918223A (en) * 1996-07-22 1999-06-29 Muscle Fish Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information
US6088455A (en) * 1997-01-07 2000-07-11 Logan; James D. Methods and apparatus for selectively reproducing segments of broadcast programming
US20010044719A1 (en) * 1999-07-02 2001-11-22 Mitsubishi Electric Research Laboratories, Inc. Method and system for recognizing, indexing, and searching acoustic signals
US20020023020A1 (en) * 1999-09-21 2002-02-21 Kenyon Stephen C. Audio identification system and method
US20020072982A1 (en) * 2000-12-12 2002-06-13 Shazam Entertainment Ltd. Method and system for interacting with a user in an experiential environment
US6434520B1 (en) * 1999-04-16 2002-08-13 International Business Machines Corporation System and method for indexing and querying audio archives
US6453252B1 (en) * 2000-05-15 2002-09-17 Creative Technology Ltd. Process for identifying audio content
US6480825B1 (en) * 1997-01-31 2002-11-12 T-Netix, Inc. System and method for detecting a recorded voice
US6483927B2 (en) * 2000-12-18 2002-11-19 Digimarc Corporation Synchronizing readers of hidden auxiliary data in quantization-based data hiding schemes
US20040199387A1 (en) * 2000-07-31 2004-10-07 Wang Avery Li-Chun Method and system for purchasing pre-recorded music
US6990453B2 (en) * 2000-07-31 2006-01-24 Landmark Digital Services Llc System and methods for recognizing sound and music signals in high noise and distortion
US7082394B2 (en) * 2002-06-25 2006-07-25 Microsoft Corporation Noise-robust feature extraction using multi-layer principal component analysis
US20060277047A1 (en) * 2005-02-08 2006-12-07 Landmark Digital Services Llc Automatic identification of repeated material in audio signals
US7194752B1 (en) * 1999-10-19 2007-03-20 Iceberg Industries, Llc Method and apparatus for automatically recognizing input audio and/or video streams
US7328153B2 (en) * 2001-07-20 2008-02-05 Gracenote, Inc. Automatic identification of sound recordings

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5940799A (en) 1997-09-15 1999-08-17 Motorola, Inc. System and method for securing speech transactions
US5913196A (en) 1997-11-17 1999-06-15 Talmor; Rita System and method for establishing identity of a speaker
EP1147511A1 (en) * 1999-07-08 2001-10-24 Constantin Alexiou Method of automatic recognition of musical compositions and sound signals
DE60228202D1 (en) * 2001-02-12 2008-09-25 Gracenote Inc A method for generating an identification hash of the contents of a multimedia file
CN1708758A (en) * 2002-11-01 2005-12-14 皇家飞利浦电子股份有限公司 Improved audio data fingerprint searching
KR100456408B1 (en) * 2004-02-06 2004-11-10 (주)뮤레카 Search of audio date and sample

Patent Citations (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4415767A (en) * 1981-10-19 1983-11-15 Votan Method and apparatus for speech recognition and reproduction
US4450531A (en) * 1982-09-10 1984-05-22 Ensco, Inc. Broadcast signal recognition system and method
US4843562A (en) * 1987-06-24 1989-06-27 Broadcast Data Systems Limited Partnership Broadcast information classification system and method
US5210820A (en) * 1990-05-02 1993-05-11 Broadcast Data Systems Limited Partnership Signal recognition system and method
US5918223A (en) * 1996-07-22 1999-06-29 Muscle Fish Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information
US6088455A (en) * 1997-01-07 2000-07-11 Logan; James D. Methods and apparatus for selectively reproducing segments of broadcast programming
US6480825B1 (en) * 1997-01-31 2002-11-12 T-Netix, Inc. System and method for detecting a recorded voice
US6434520B1 (en) * 1999-04-16 2002-08-13 International Business Machines Corporation System and method for indexing and querying audio archives
US20010044719A1 (en) * 1999-07-02 2001-11-22 Mitsubishi Electric Research Laboratories, Inc. Method and system for recognizing, indexing, and searching acoustic signals
US7174293B2 (en) * 1999-09-21 2007-02-06 Iceberg Industries Llc Audio identification system and method
US20020023020A1 (en) * 1999-09-21 2002-02-21 Kenyon Stephen C. Audio identification system and method
US7194752B1 (en) * 1999-10-19 2007-03-20 Iceberg Industries, Llc Method and apparatus for automatically recognizing input audio and/or video streams
US6453252B1 (en) * 2000-05-15 2002-09-17 Creative Technology Ltd. Process for identifying audio content
US20040199387A1 (en) * 2000-07-31 2004-10-07 Wang Avery Li-Chun Method and system for purchasing pre-recorded music
US6990453B2 (en) * 2000-07-31 2006-01-24 Landmark Digital Services Llc System and methods for recognizing sound and music signals in high noise and distortion
US20060122839A1 (en) * 2000-07-31 2006-06-08 Avery Li-Chun Wang System and methods for recognizing sound and music signals in high noise and distortion
US20020072982A1 (en) * 2000-12-12 2002-06-13 Shazam Entertainment Ltd. Method and system for interacting with a user in an experiential environment
US6483927B2 (en) * 2000-12-18 2002-11-19 Digimarc Corporation Synchronizing readers of hidden auxiliary data in quantization-based data hiding schemes
US7328153B2 (en) * 2001-07-20 2008-02-05 Gracenote, Inc. Automatic identification of sound recordings
US7082394B2 (en) * 2002-06-25 2006-07-25 Microsoft Corporation Noise-robust feature extraction using multi-layer principal component analysis
US20060277047A1 (en) * 2005-02-08 2006-12-07 Landmark Digital Services Llc Automatic identification of repeated material in audio signals

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090012638A1 (en) * 2007-07-06 2009-01-08 Xia Lou Feature extraction for identification and classification of audio signals
US8140331B2 (en) * 2007-07-06 2012-03-20 Xia Lou Feature extraction for identification and classification of audio signals
US8831763B1 (en) * 2011-10-18 2014-09-09 Google Inc. Intelligent interest point pruning for audio matching
US20130255473A1 (en) * 2012-03-29 2013-10-03 Sony Corporation Tonal component detection method, tonal component detection apparatus, and program
US8779271B2 (en) * 2012-03-29 2014-07-15 Sony Corporation Tonal component detection method, tonal component detection apparatus, and program
US9129015B1 (en) * 2012-06-26 2015-09-08 Google Inc. Min/max filter for audio matching
US9390719B1 (en) * 2012-10-09 2016-07-12 Google Inc. Interest points density control for audio matching
US9069849B1 (en) * 2012-10-10 2015-06-30 Google Inc. Methods for enforcing time alignment for speed resistant audio matching

Also Published As

Publication number Publication date Type
KR100820385B1 (en) 2008-04-10 grant
DK1504445T3 (en) 2008-12-01 grant
EP1504445A1 (en) 2005-02-09 application
EP1504445B1 (en) 2008-08-20 grant
WO2003091990A1 (en) 2003-11-06 application
CA2483104A1 (en) 2003-11-06 application
ES2312772T3 (en) 2009-03-01 grant
EP1504445A4 (en) 2005-08-17 application
KR20050010763A (en) 2005-01-28 application
US7627477B2 (en) 2009-12-01 grant
CN1647160A (en) 2005-07-27 application
US20050177372A1 (en) 2005-08-11 application
CN1315110C (en) 2007-05-09 grant
JP2005524108A (en) 2005-08-11 application
DE60323086D1 (en) 2008-10-02 grant
CA2483104C (en) 2011-06-21 grant
JP4425126B2 (en) 2010-03-03 grant

Similar Documents

Publication Publication Date Title
US6774917B1 (en) Methods and apparatuses for interactive similarity searching, retrieval, and browsing of video
US6968337B2 (en) Method and apparatus for identifying an unknown work
US7170566B2 (en) Family histogram based techniques for detection of commercials and other video content
US7336890B2 (en) Automatic detection and segmentation of music videos in an audio/video stream
US6393398B1 (en) Continuous speech recognizing apparatus and a recording medium thereof
US20020028021A1 (en) Methods and apparatuses for video segmentation, classification, and retrieval using image class statistical models
US20090022472A1 (en) Method and Apparatus for Video Digest Generation
US5712953A (en) System and method for classification of audio or audio/video signals based on musical content
US20050249080A1 (en) Method and system for harvesting a media stream
US20030161396A1 (en) Method for automatically producing optimal summaries of linear media
Leveau et al. Instrument-specific harmonic atoms for mid-level music representation
US7672836B2 (en) Method and apparatus for estimating pitch of signal
US7273978B2 (en) Device and method for characterizing a tone signal
US6744922B1 (en) Signal processing method and video/voice processing device
Ajmera et al. Robust speaker change detection
Hanjalic Generic approach to highlights extraction from a sport video
US8335786B2 (en) Multi-media content identification using multi-level content signature correlation and fast similarity search
US20080226173A1 (en) Method and apparatus for video clip searching and mining
US20040068401A1 (en) Device and method for analysing an audio signal in view of obtaining rhythm information
US20060041753A1 (en) Fingerprint extraction
US20100205174A1 (en) Audio/Video Fingerprint Search Accuracy Using Multiple Search Combining
US5774836A (en) System and method for performing pitch estimation and error checking on low estimated pitch values in a correlation based pitch estimator
Li et al. Classification of general audio data for content-based retrieval
US6996171B1 (en) Data describing method and data processor
Papadimitriou et al. Local correlation tracking in time series

Legal Events

Date Code Title Description
AS Assignment

Owner name: SHAZAM ENTERTAINMENT, LTD., UNITED KINGDOM

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WANG, AVERY LI-CHUN;CULBERT, DANIEL;REEL/FRAME:015946/0539

Effective date: 20030418

AS Assignment

Owner name: LANDMARK DIGITAL SERVICES LLC, TENNESSEE

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SHAZAM ENTERTAINMENT LIMITED;REEL/FRAME:016546/0813

Effective date: 20050826

AS Assignment

Owner name: LANDMARK DIGITAL SERVICES LLC, TENNESSEE

Free format text: CORRECTIVE ASSIGNMENT TO RE-RECORD ASSIGNMENT PREVIOUSLY RECORDED UNDER REEL AND FRAME 0165;ASSIGNOR:SHAZAM ENTERTAINMENT LIMITED;REEL/FRAME:016551/0257

Effective date: 20050826

Owner name: LANDMARK DIGITAL SERVICES LLC, TENNESSEE

Free format text: CORRECTIVE ASSIGNMENT TO RE-RECORD ASSIGNMENT PREVIOUSLY RECORDED UNDER REEL AND FRAME 016546/0813 TO CORRECT THE ADDRESS FROM 10 MUSIC SQUARE, EAST NASHVILLE, TENNESSEE 37203 TO 10 MUSIC SQUARE EAST, NASHVILLE, TENNESSEE 37203;ASSIGNOR:SHAZAM ENTERTAINMENT LIMITED;REEL/FRAME:016551/0257

Effective date: 20050826

AS Assignment

Owner name: SHAZAM INVESTMENTS LIMITED, UNITED KINGDOM

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:LANDMARK DIGITAL SERVICES LLC;REEL/FRAME:027274/0799

Effective date: 20111121

FPAY Fee payment

Year of fee payment: 4

FPAY Fee payment

Year of fee payment: 8