JP5879813B2 - Multiple sound source identification device and information processing device linked to multiple sound sources - Google Patents

Description

  The present invention relates to a technique for identifying a sound source that emits an acoustic signal such as an acoustic instrument, a natural sound, a sound generated by a human being or an organism, an artificial machine excluding an electronic instrument, and more particularly to a technique for identifying a plurality of sound sources.

  As a method of utilizing a musical instrument as a user interface of various digital information terminals, a technique using an electronic musical instrument compliant with the MIDI standard (Musical Instrument Digital Interface) is known. Keyboard instruments (keyboards, auto-playing pianos), silent stringed instruments, silent wind instruments, silent drums, etc. that have been developed in accordance with the MIDI standard have already been developed. Patent Document 1).

Japanese Patent No. 3717640 Japanese Patent No. 3935745 JP 2011-107265 A JP 2007-325201 A Japanese Patent No. 4156252

  However, applicable instruments are limited to electronic instruments that conform to the MIDI standard. On the other hand, there are a myriad of acoustic instruments in the world. On the other hand, a method using an encoding technique for converting an acoustic signal output from an acoustic instrument as disclosed in Patent Document 2 into MIDI data is conceivable. In the invention of Patent Document 2, it is possible to recognize the rhythm and scale to be played and pass the information terminal in a form compliant with the MIDI standard. However, since the timbre cannot be recognized, it is difficult to specify the played instrument. Furthermore, when a plurality of musical instruments are pronounced simultaneously, it is more difficult to specify each musical instrument.

  As such a sound source separation technique, a technique using a plurality of microphones or a microphone array has been proposed (see Patent Document 4). However, the apparatus is large and expensive, and the sound source is physically like a musical instrument sound. Is difficult to separate. On the other hand, the applicant also uniformly encoded a single sound signal recorded by synthesizing a plurality of musical instrument sounds into MIDI format, and encoded using a predefined tone color management table. A method for separating MIDI data into a plurality of channels has been proposed (see Patent Document 5). However, it cannot be separated with high precision unless the timbres have characteristics that are remarkably different from each other, such as piano and vocal, piano and guitar, and there is a problem that separation is difficult in a combination such as a grand piano and an electric piano.

  Also, in recent years, as a user interface of various information terminals such as digital signage, a part of voice recognition input has begun to be introduced as an alternative to existing digital input such as a keyboard, mouse, touch panel and the like. However, there is a problem that the voice input accuracy is remarkably poor in a crowd where a signage is installed, and the output voice cannot be heard clearly, and voice input / output is not so popular in digital signage. Thus, there is no example of a user interface using sound inputs such as instrument sounds other than voice, and there is no means for substituting a user interface using a plurality of acoustic instruments.

  Therefore, the present invention provides a multi-sound source identification device capable of specifying each sound source from sounds simultaneously generated by a plurality of sound sources such as acoustic instruments, natural sounds, humans / living organisms, and information processing linked to the plurality of sound sources. It is an object to provide an apparatus.

In order to solve the above-mentioned problem, in the first aspect of the present invention, a device for acquiring sounds emitted from a plurality of sound sources and identifying each sound source, the registered feature data expressing the features of each sound source A sound source database in which identification information for identifying each sound source is associated and registered; sound signal acquisition means for recording and acquiring sounds emitted from the plurality of sound sources as digital sound signals; and Frequency characteristic analysis, and feature data generation means for generating search feature data based on a search side average spectrum that is a spectrum averaged over time, and the generated search feature data and the sound source database are registered. Performs correlation calculation with each registered feature data, and has the maximum correlation value among the obtained correlation values, and the correlation value satisfies a predetermined threshold value or more. A sound source database search unit that specifies a sound source corresponding to registered feature data based on the identification information, and using the registered feature data corresponding to the sound source specified by the sound source database search unit, the search feature data is corrected, and a corrected feature Feature data correction means for generating data, and the sound source database search means includes the generated correction feature data and registered feature data registered in the sound source database excluding the already specified sound source. Using the registered feature data registered in the sound source database in association with the sound source specified by the sound source database search unit. The registration side average scan, which is a temporally averaged spectrum that is the basis for creating the registration feature data. For the frequency number that gives the largest component of the created registration-side average spectrum, the corresponding value of the search-side average spectrum is divided by the largest component of the registration-side average spectrum, and the value is For each frequency number including the frequency number that gives the maximum component, the multiplication by the registration-side average spectrum corresponding to the specified sound source is subtracted from the search-side average spectrum, thereby the frequency number corresponding to each frequency number. The search-side average spectrum is corrected, and the sound source database search means and the feature data correction means provide a plurality of sound source identification devices characterized by repeatedly performing processing until a predetermined condition is satisfied.

According to the first aspect of the present invention, frequency analysis is performed on an acoustic signal obtained by digitizing sound emitted from a sound source, and search feature data is generated based on a temporally averaged spectrum. After specifying registered feature data having a high correlation with each registered feature data, the search feature data is corrected using the specified registered feature data to obtain corrected feature data. The corrected feature data and the sound source database Since the process of specifying registered feature data having a high correlation with each registered feature data is repeatedly executed, each sound source can be specified from sounds simultaneously generated by a plurality of sound sources. Further, according to the first aspect of the present invention, the registration-side average spectrum Vd (j) that is the basis for creating the registration feature data is registered, and the registration-side average spectrum Vd (j) corresponding to the specified sound source is registered. Is divided by the maximum component Vdmax (jmax) of the registration-side average spectrum Vdmax (j), and the corresponding value Vq (jmax) of the search-side average spectrum is divided by the frequency number jmax. The search side average spectrum Vq corresponding to all the frequencies j is obtained by subtracting the frequency obtained by multiplying the registration side average spectrum Vdmax (j) from the search side average spectrum Vq (j) for all frequency numbers j including jmax. Since (j) was corrected and the search feature data was corrected, it was specified from the search side average spectrum of the acquired acoustic signal. The portion corresponding to the source-side registered average spectrum can be removed, and the corrected search-side average spectrum represents the sound source excluding the identified first sound source, so that the second and subsequent sounds are accurate. It becomes possible to specify.

In the second aspect of the present invention, an apparatus for acquiring sounds emitted from a plurality of sound sources and identifying each sound source, for each sound source, specifying registered feature data representing the feature and each sound source A sound source database in which identification information is registered in association with each other;
An acoustic signal acquiring means for acquiring a digital audio signal to record the sound emitted from the plurality of sound sources, the performs frequency analysis on the acoustic signal, the searching side average of the spectrum is a spectrum time-averaged Feature data generating means for generating search feature data based on a value obtained by multiplying each frequency component by a weight that increases as the frequency value increases, and registration in the generated search feature data and the sound source database A sound source corresponding to registered feature data having a maximum correlation value among the obtained correlation values and satisfying the correlation value equal to or higher than a predetermined threshold among the obtained correlation values. Sound source database search means for specifying the identification information based on the identification information, and registered feature data corresponding to the sound source specified by the sound source database search means And a feature data correction unit that corrects the search feature data and generates corrected feature data, wherein the sound source database search unit excludes the generated correction feature data and the already specified sound source. Using the registered feature data registered in the sound source database, the process of specifying the sound source is executed, and the sound source database search means and the feature data correction means repeatedly execute processing until a predetermined condition is satisfied. A multi-sound source identification device is provided.

According to the second aspect of the present invention , frequency analysis is performed on an acoustic signal obtained by digitizing sound emitted from a sound source, and search feature data is generated based on a temporally averaged spectrum. After specifying registered feature data having a high correlation with each registered feature data, the search feature data is corrected using the specified registered feature data to obtain corrected feature data. The corrected feature data and the sound source database Since the process of specifying registered feature data having a high correlation with each registered feature data is repeatedly executed, each sound source can be specified from sounds simultaneously generated by a plurality of sound sources. According to the second aspect of the present invention, feature data is generated by multiplying each frequency component of the search-side average spectrum, which is a spectrum averaged over time, by a weight that increases as the frequency value increases. Therefore, even if the difference between the sound sources is a slight difference in high-frequency components, the difference becomes clear, and the characteristics of the sound emitted by acoustic instruments, natural sounds, human beings, organisms, etc. are clarified Since it can be identified, the correct sound source can be specified.

In the third aspect of the present invention, the feature data correction means in the multiple sound source identification device of the second aspect of the present invention includes registered feature data corresponding to the sound source specified by the sound source database search means, and the search feature data The correction feature data is generated by obtaining a correlation value V1 and subtracting a value obtained by multiplying the specified registered feature data by the correlation value V1 from the search feature data.

  According to the third aspect of the present invention, the registered feature data corresponding to the specified sound source and the correlation value V1 of the search feature data are obtained, and the search feature data obtained by multiplying the specified registered feature data by the correlation value V1. Since the correction feature data is generated by subtracting from the correction feature data, the correction feature data excluding the features of the first sound source can be generated with high accuracy while quantitatively calculating the component ratio of the first sound source from the search feature data. It is possible to specify the second and subsequent sound sources with high accuracy while calculating the component ratios for the second and subsequent sound sources in the same manner.

In the fourth aspect of the present invention, the feature data generating means in the multiple sound source identification device of the first aspect of the present invention increases as the frequency value increases for each frequency component of the search side average spectrum. The search feature data is generated based on a value multiplied by a weight.

  According to the fourth aspect of the present invention, feature data is generated by multiplying each frequency component of the search-side average spectrum, which is a spectrum averaged over time, by a weight that increases as the frequency value increases. Therefore, even if the difference between sound sources is a slight difference in high-frequency components, the difference becomes clear, and the characteristics of the emitted sound such as acoustic instruments, natural sounds, human beings and living things are clearly identified Therefore, it is possible to specify the correct sound source.

In the fifth aspect of the present invention, the feature data generation means in the multiple sound source identification apparatus according to any one of the second to fourth aspects of the present invention provides the frequency value of each frequency component as the search feature data. The deviation vector is generated by subtracting the average value of the values multiplied by the weights that increase as the frequency value increases from the value obtained by multiplying the weights that increase as the frequency value increases.

  According to the fifth aspect of the present invention, as the search feature data, the deviation vector is generated by subtracting the average value of each frequency component multiplied by a different weight, so that the characteristic frequency component is more clearly defined. Thus, each sound source can be easily identified.

  In the sixth aspect of the present invention, the characteristic data generating means in the multi-sound source identification device according to any one of the first to fifth aspects of the present invention is configured such that the amplitude of the acoustic signal acquired by the acoustic signal acquiring means is a predetermined value. A silence interval that is less than or equal to a predetermined time is specified, correction is performed to delete the specified silence interval to shorten the acoustic signal in time, and the frequency analysis is performed on the corrected acoustic signal. It is characterized by performing.

  According to the sixth aspect of the present invention, since the silent section of the acoustic signal is deleted before performing the frequency analysis, the interval between the silent section and the sound generation section fluctuates with respect to the spectrum that is the basis of the feature data. However, it is possible to prevent the influence on the identification of the sound source.

In the seventh aspect of the present invention, the feature data generating means in the multiple sound source identification device according to any one of the first to sixth aspects of the present invention performs the frequency analysis as a unit of a predetermined section for the acoustic signal. The waveform data is divided and subjected to Fourier transform on the waveform data in which the weight function defined in advance is superimposed on the waveform data of each section, and the mean square value of the real part and the imaginary part is taken for each section. real phased spectra over the entire interval, characterized in that obtaining the searching side average spectrum by averaging Te.

  According to the seventh aspect of the present invention, as the frequency analysis, the acoustic signal is divided into predetermined sections, Fourier transform is performed on the waveform data of each section, and the real spectrum obtained for each section is converted into all sections. Therefore, it is possible to generate search feature data that is a feature over the entire section without reducing the time resolution of the frequency analysis.

  According to an eighth aspect of the present invention, in the multiple sound source identification device according to the seventh aspect of the present invention, each of the sections overlaps by a half time width of the section length between adjacent sections, and the weight function is Two types of asymmetric shapes are defined with respect to the time axis direction. One weight function is superimposed on the odd-numbered section, and the other weight function is superimposed on the even-numbered section. It is characterized by doing so.

  According to the eighth aspect of the present invention, each interval is overlapped by two intervals between adjacent intervals, and two types of weighting functions are defined in an asymmetric shape with respect to the time axis direction. Since different weighting functions are superimposed on the first and even-numbered sections, it is possible to distinguish between a reverse waveform obtained by reproducing the acoustic signal from the reverse and a sound obtained by normally reproducing the original acoustic signal.

According to a ninth aspect of the present invention , there is provided a program that uses the identification information specified by the multiple sound source identification apparatus according to any one of the first to eighth aspects of the present invention, and performs a different process for each identification information. stores the, start the program corresponding to the identification information output from the identification device of the plurality of sound sources, to provide an information processing apparatus that works in multiple sound source that perform processing according to the program.

  According to the ninth aspect of the present invention, since the information processing apparatus executes the process based on the identification information specified by the identification apparatus for a plurality of sound sources, the sound source can be obtained simply by pointing the microphone to the emitted sound. The processing corresponding to is executed.

  According to the present invention, it is possible to specify each sound source from sounds simultaneously generated by a plurality of sound sources such as an acoustic instrument, a natural sound, and humans / living organisms.

It is a hardware block diagram of a registration characteristic data generation apparatus. It is a functional block diagram of a registration feature data generation device. It is a flowchart which shows the processing operation of the apparatus shown in FIG. It is a figure for demonstrating deletion of a silence area. It is a figure which shows the case where the spectrum proportional to a frequency value is multiplied by the value of a musical instrument as a weight. It is a hardware block diagram of the identification device of multiple sound sources according to the present invention. It is a functional block diagram of the identification apparatus of the multiple sound source which concerns on this invention. 8 is a flowchart showing a processing operation of the apparatus shown in FIGS. It is a flowchart which shows the detail of the search process of the sound source in S230 of FIG. It is a figure which shows the amplitude change and reverse rotation waveform of a sound. It is a figure which shows the window function used by this invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
(1. Preparation of sound source database)
In the present invention, the frequency of sound emitted from a sound source is analyzed, and a spectrum for each sound source is generated. Feature data representing the features of the sound source is generated based on the generated spectrum, and recorded in the sound source database together with identification information of the sound source. Then, the sound to be identified is acquired, frequency analysis is performed to generate feature data, and the sound source of the sound is specified by comparing and collating with the feature data in the database.

  Therefore, first, it is necessary to prepare a sound source database storing feature data corresponding to the sound source. The feature data to be registered in the sound source database is generated by a registered feature data generation device. FIG. 1 is a hardware configuration diagram of a registered feature data generation apparatus. The registered feature data generation device can be realized by a general-purpose computer. As shown in FIG. 1, a CPU 1 (CPU: Central Processing Unit), a main memory RAM 2 (RAM: Random Access Memory), A large-capacity storage device 3 (for example, a hard disk, a flash memory, etc.) for storing programs and data executed by the CPU, a key input I / F (interface) 4 such as a keyboard and a mouse, and an external device (data storage) A data input / output I / F (interface) 5 for data communication with a medium or the like, and a display output I / F (interface) 6 for sending information to a display device (liquid crystal display or the like). Connected via bus.

  FIG. 2 is a functional block diagram showing the configuration of the registered feature data generation apparatus. In FIG. 2, 10 is a silent section deleting means, 20 is an acoustic frame reading means, 30 is a feature data generating means, 40 is a feature data registering means, and 50 is a sound source database.

  The silent section deleting means 10 has a function of deleting a section (silent section) determined to be silent from the digital audio signal. The sound frame reading means 20 has a function of sequentially reading a predetermined number N of samples as one sound frame from a digital sound signal recorded with a certain sound as a material. The feature data generation unit 30 has a function of performing frequency analysis using the read sound frame and generating feature data representing the feature of the sound signal for the sound source. This feature data represents a feature of a certain acoustic signal with a small amount of data, and is a vector of maximum N / 2 dimensions as will be described later. In the present invention, the sound source is specified by comparing the registered feature data registered in the sound source database with the search feature data generated from the sound signal obtained by recording the sound. Therefore, the registered feature data and the search feature data need to be in the same format. Therefore, the registration feature data and the search feature data are generated in the same method, and in this specification, the acoustic signal obtained by recording the sound using the registered feature data as the registered feature data. The feature data generated from is called search feature data. The feature data registration unit 40 associates the generated feature data with the related information related to the sound source corresponding to the original sound signal and the sound source ID which is identification information for specifying the sound source as the registered feature data as the sound source database. 50 is registered. As described above, the sound source indicates a sound source such as a musical instrument, a natural object, a living thing, or a natural phenomenon. Each component shown in FIG. 2 is realized by installing a dedicated program in the hardware configuration shown in FIG.

  In the storage device 3 of FIG. 1, a dedicated program for operating the CPU 1 and causing the computer to function as a registered feature data generation device is installed. By executing this dedicated program, the CPU 1 realizes functions as the silent section deleting unit 10, the acoustic frame reading unit 20, the feature data generating unit 30, and the feature data registering unit 40. The storage device 3 stores various data necessary for processing.

  Next, the processing operation of the registered feature data generation apparatus shown in FIGS. 1 and 2 will be described with reference to the flowchart of FIG. First, the registered feature data generation device reads a digital acoustic signal from an acoustic signal file in which a sound source with known identification information prepared in advance is recorded. This digital sound signal is obtained by sampling an analog sound signal by a technique such as PCM. For example, when the sampling frequency is 44.1 kHz, 44100 samples are obtained per second.

  In the registered feature data generation apparatus, after reading the digital acoustic signal, the silent section deleting means 10 deletes the silent section (S101). Specifically, when a predetermined number of samples whose sample values are less than a predetermined threshold value continue, it is determined that the period is a silent period, and the silent period is deleted. The threshold value of each sample and the number of consecutive silent samples can be set as appropriate. In this embodiment, on the condition that the sampling frequency is 44.1 kHz and the quantization bit number is 16 bits (−32768 to +32767 in decimal number), the threshold value of the absolute value of each sample is 1000 (decimal number), and the silence samples are continuous. The number is 2048. The number of consecutive silence samples 2048 corresponds to about 0.046 seconds when the sampling frequency is 44.1 kHz.

  FIG. 4 is a diagram for explaining deletion of a silent section. In FIG. 4, each sample value of the acoustic signal is shown as a waveform connected by line segments. As shown in FIG. 4, the silent section is deleted by not removing the sample value of the silent section but setting the sample of the subsequent sounding section to the front. Therefore, as a result of the silent section deletion process in S101, the total number of samples of the read sound signal is reduced, and the reproduction time when reproduced as it is is shortened. Depending on the characteristics of the sound source, it is not always necessary to delete the silent section. However, in the case of a type in which short sounding intervals are arranged intermittently, such as percussion instrument sounds, the average spectrum from the start to the end varies greatly depending on the ratio of the silent interval to the sounding interval (performance rhythm). . For example, the drum sound A prepared when registering the feature data shown in FIGS. 2 and 3 and the drum sound B captured by the microphone 60 shown in FIG. Generally, it is different, but by executing this silent section deletion process in both the registration process as shown in FIGS. 2 and 3 and the identification process as shown in FIGS. The difference in rhythm between the sound A and the drum sound B is absorbed, and the drum sound A and the drum sound B can be determined to be the same sound source. For this reason, it is effective to delete the silent section for a type of sound source in which short sound generation sections are arranged intermittently.

  When the deletion of the silent section is finished, the acoustic frame reading means 20 reads a predetermined number of samples as one acoustic frame from the acoustic signal. The number of samples of one sound frame read by the sound frame reading means 20 can be set as appropriate, but is desirably about 4096 samples when the sampling frequency is 44.1 kHz. This corresponds to about 0.093 seconds. However, in consideration of samples whose values decrease due to the use of a window function in frequency conversion described later, the acoustic frame is read by overlapping a predetermined number of samples. In this embodiment, 2048 samples that are exactly ½ of the section length of the acoustic frame are overlapped. Therefore, the first acoustic frame is sequentially read as samples 1 to 4096, the second acoustic frame is samples 2049 to 6144, and the third acoustic frame is samples 4097 to 8192.

  Subsequently, the feature data generation unit 30 performs frequency conversion on each read sound frame to obtain a frame spectrum that is a spectrum of the sound frame (S102). Specifically, frequency conversion is performed on the acoustic frame read by the acoustic frame reading means 20 using a window function. As frequency conversion, Fourier transform, wavelet transform, and other various known methods can be used. In the present embodiment, a case where Fourier transform is used will be described as an example.

  Here, a window function used for Fourier transform in the present embodiment will be described. In general, when performing Fourier transform on a predetermined signal, it is necessary to divide the signal into predetermined lengths. In this case, if Fourier transform is performed on a signal having a predetermined length, A pseudo component is generated. Therefore, in general, when performing Fourier transform, a signal value is changed using a window function called a Hanning window, and then Fourier transform is performed on the changed value.

  When performing the Fourier transform in S102, specifically, the Hanning window function W (i) (= 0.5-0) is applied to the value X (i) (i = 0,..., N-1) in the sample i. .5 cos (2πi / N)), the processing according to the following [Equation 1] is performed. This is executed for each acoustic frame g (g = 0,..., G−1) to obtain a real part A (g, j) and an imaginary part B (g, j) at each frequency of each acoustic frame g. .

[Formula 1]
A (g, j) = Σ _{i = 0,..., N−1} W (i) · X (i) · cos (2πij / N)
B (g, j) = Σ _{i = 0,..., N−1} W (i) · X (i) · sin (2πij / N)

  Subsequently, the feature data generating unit 30 performs averaging in the time direction (S103). Specifically, processing according to the following [Equation 2] is performed to obtain time-average spectra V (j) of G sound frames at each frequency.

[Formula 2]
V (j) = [Σ _{g = 0,..., G−1} {A (g, j) ² + B (g, j) ² } / G] ^1/2

  In the above [Equation 1] and [Equation 2], i is a serial number assigned to N samples in each acoustic frame, and takes an integer value of i = 0, 1, 2,. Further, j is a serial number assigned in order from the smallest value of the frequency value, and the lower half excluding the aliasing component is used from the calculated spectrum. Therefore, an integer value of j = 1, 2,... N / 2 is taken. When the sampling frequency is 44.1 kHz and N = 4096, if the value of j is different by one, the frequency will be different by 10.8 Hz. As a result of the processing according to the above [Equation 2], a time average spectrum V (j) at each frequency obtained by averaging all G acoustic frames is obtained.

  Subsequently, the feature data generating unit 30 calculates a deviation vector (S104). Specifically, each frequency component of the time-average spectrum V (j) averaged over all acoustic frames is multiplied by a value j that increases as the frequency value increases, and for each frequency component, Performs processing to reduce the average of all frequencies. Actually, the feature data generation unit 30 executes processing according to the following [Equation 3] to calculate the average value Av.

[Formula 3]
Av = Σ _{j = f1,..., F2} V (j) · j / (f2−f1 + 1)

  In the above [Equation 3], for each frequency between the frequency numbers f1 and f2, an average value Av of the time average spectrum V (j) multiplied by the frequency number j is calculated. The frequency numbers f1 and f2 can be appropriately set according to the frequency band of the acoustic signal that is the basis of the registered feature word registered in the sound source database. In this embodiment, f1 = 27 ( F2 = 743 (corresponding to approximately 8000 Hz).

  Then, the feature data generation unit 30 executes processing according to the following [Equation 4] to calculate the deviation vector δV (j) as feature data.

[Formula 4]
δV (j) = V (j) · j−Av

  Here, the reason why the weight of the frequency number j is multiplied when the deviation vector is used as the feature data will be described. The spectrum of the sound source that is the basis of the feature data has a harmonic structure, and as shown in FIGS. 5A and 5B, the signal component tends to decrease in inverse proportion to the frequency. In particular, when a musical instrument is used as a sound source, timbre characteristics often appear in high-order overtones, so that differences in high-frequency components are not noticeable. Therefore, in this embodiment, a spectrum obtained by multiplying a value proportional to the frequency value as a weight is generated as feature data, and the difference between the high-frequency components is made to stand out, thereby facilitating identification of each sound source. For example, when the spectrums of musical instruments A and B shown in FIGS. 5A and 5B are multiplied by a value proportional to the frequency value as a weight, a weighted spectrum as shown in FIGS. 5C and 5D is obtained. It is done. Since a larger weight is applied to become a high frequency component, the difference in the high frequency component becomes conspicuous as is clear when the waveforms in FIGS. 5C and 5D are compared.

  As described above, the feature data δV (j) that is a deviation vector is generated from the sound signal of a certain sound source. This feature data δV (j) is a deviation vector of maximum N / 2 dimensions (in the case of N = 4096, 2048 dimensions). However, the dimension becomes (f2-f1 + 1) according to f1 and f2 set as described above. Therefore, when f1 = 27 and f2 = 743 are set, there are 717 dimensions. When the feature data δV (j) is generated, the feature data registration unit 40 associates the feature data δV (j) and the time average spectrum V (j) with the related information such as the separately input sound ID and sound source name. Register in the sound source database 50. Both feature data δV (j) and time-average spectrum V (j) are required in the process of searching for a plurality of sound sources, which will be described later. However, feature data δV (j) is originally time-average spectrum V (j ) Is calculated based on [Equation 3] and [Equation 4], and in reality, only the time average spectrum V (j) may be stored in a predetermined area in the storage device 3, On the contrary, the time average spectrum V (j) can be calculated in reverse by modifying [Equation 4] using the feature data δV (j) and the average value Av of the spectrum, so that the feature data δV (j) The spectrum average value Av may be stored in a predetermined area in the storage device 3. The feature data δV (j) and the time average spectrum V (j) registered in the sound source database are hereinafter registered in order to distinguish from the feature data and time average spectrum generated from the sound signal obtained by recording at the time of identification. Data δVd (j) and registration side average spectrum Vd (j) are expressed.

(2. Identification of sound source)
Next, a multi-sound source identification device according to the present invention will be described. FIG. 6 is a hardware configuration diagram of the multiple sound source identification apparatus according to the present invention. Similar to the registered feature data generation device, the multiple sound source identification device can be realized by a general-purpose computer. As shown in FIG. 6, a CPU 1a (CPU: Central Processing Unit) and a RAM 2a (main memory of the computer) RAM (Random Access Memory), a large-capacity storage device 3a (for example, a hard disk, a flash memory, etc.) for storing programs and data executed by the CPU 1a, and a key input I / F (interface) such as a keyboard and a mouse 4a, a data input / output I / F (interface) 5a for data communication with an external device (data storage medium or the like), and a display output I / F (interface) 6a for sending information to a display device (display) And an audio input I / F (interface) 7a connected to a microphone. It is connected via a bus.

FIG. 7 is a functional block diagram showing a configuration of a multiple sound source identification device according to the present invention. In FIG. 7, 10 is a silent section deleting means, 20 is an acoustic frame reading means, 30 is a feature data generating means, 31 is a feature data correcting means, 50 is a sound source database, 60 is a microphone, 70 is an acoustic signal acquiring means, and 80 is A sound source database search means 90 is a sound source information output means. Components having the same reference numerals as those in FIG. 2 have the same functions as those in FIG. The process of FIG. 7 is started based on an instruction from the user, but is repeatedly executed until an instruction to stop is received from the user in order to capture the sound of the identification target emitted from the user with good timing. Takes form. That is, After running output of the sound source information of the last, so as to perform the process returns following similar processing to acquisition of the acoustic signal.

  The microphone 60 does not need to have the ability to faithfully capture a wide range of sound from low frequency components to high frequency components, and uses an inexpensive consumer microphone built into various portable terminals such as smartphones and portable game machines. Can do. Specifically, in the creation of the feature data as described above, as shown in the above [Equation 3], only the frequency range from the frequency number f1 = 27 (corresponding to about 300 Hz) to f2 = 743 (corresponding to about 8000 Hz). Since it is not a calculation target, the necessary performance of the microphone 60 only needs to cover this range (a microphone sold as a product covers at least this frequency range). The acoustic signal acquisition means 70 has a function of A / D converting and digitizing an analog acoustic signal captured by the microphone 60.

  The sound source database searching unit 80 has a function of collating the generated feature data with the registered feature data registered in the sound source database 50. The sound source information output means 90 identifies identification information (such as a code number for identifying a musical instrument, a program number for MIDI standards) about the sound source most similar to the characteristics of the acoustic signal obtained by recording as a result of the collation by the sound source database search means 80. Is extracted from the sound source database 50 and output. Each component shown in FIG. 7 is realized by installing a dedicated program in the hardware configuration shown in FIG.

  6 is mounted with a dedicated program for operating the CPU 1a and causing the computer to function as a plurality of sound source identification devices. By executing this dedicated program, the CPU 1a causes the silence interval deleting means 10, the acoustic frame reading means 20, the feature data generating means 30, the feature data correcting means 31, the acoustic signal acquiring means 70, the sound source database searching means 80, the sound source. The function as the information output means 90 is realized. The storage device 3a stores various data necessary for processing.

  Next, the processing operation of the multiple sound source identification device shown in FIGS. 6 and 7 will be described with reference to the flowchart of FIG. First, the user gives an activation instruction to the identification device for a plurality of sound sources. When the identification device for a plurality of sound sources is realized by a general-purpose computer, it can be executed by pressing a predetermined key on the keyboard or clicking a predetermined portion displayed on the screen with a mouse. The user generates a plurality of sounds to be identified for an appropriate interval (for example, 5 seconds) at an arbitrary timing after starting the identification device for a plurality of sound sources. For example, a plurality of musical instruments are simultaneously played for 5 seconds. When the instruction is input, the multi-sound source identification device takes the music flowing from the microphone 60 for a certain period (for example, 2 seconds), records it, and obtains it as a digital acoustic signal (S210). Specifically, the acoustic signal input from the microphone 60 is digitized by the acoustic signal acquisition unit 70.

  If the digital sound signal is obtained, feature data is generated from the digital sound signal (S220). Specifically, the silent section deleting unit 10, the acoustic frame reading unit 20, and the feature data generating unit 30 execute the processes of S101 to S104 shown in FIG. As a result of the feature data generation process in S220, the feature data δV (j) as shown in [Formula 4] is obtained. As described above, the format and generation method of the feature data is the same as the registered feature data δVd (j), but in order to distinguish it from the registered feature data δVd (j) and the registration-side average spectrum Vd (j), The feature data δV (j) and the time average spectrum V (j) generated in the multiple sound source identification device are expressed as search feature data δVq (j) and search side average spectrum Vq (j), respectively.

  Next, a sound source is searched using the generated feature data (S230). Details of the sound source search process will be described later. As a result of the sound source search, if the sound source corresponding to the generated feature data is successfully identified (S240), the feature data is corrected (S250). Specifically, the feature data is corrected using the registered feature data corresponding to the specified sound source to obtain corrected feature data. Details of the correction process of the feature data will be described later. After correcting the feature data, the process returns to S230, and using the obtained corrected feature data, the already identified sound source is excluded from the search target and the sound source is searched. In this way, the feature data correction (S250) and the sound source search (S230) are repeatedly executed, and the process ends when the sound source identification fails.

  In the present embodiment, it is determined whether or not the sound source has been successfully identified in S240, and the sound source is identified repeatedly until the sound source identification fails. However, the condition for ending the sound source identification is limited to the present embodiment. It may be changed. For example, the number of sound sources to be specified may be specified, and the process may be terminated when a predetermined number is reached.

  The sound source search process in S230 will be described with reference to the flowchart of FIG. When the feature data δV (j) is obtained from the acquired digital acoustic signal, the sound source database search means 80 collates with each registered feature data in the sound source database 50. First, one registered feature data is extracted from the sound source database 50, and a correlation calculation with the feature data is performed (S231). Specifically, the sound source database search means 80 executes processing according to the following [Equation 5] to calculate the correlation value RE.

  As described above, the registered feature data registered in the sound source database 50 and the search feature data generated from the acquired digital acoustic signal are both of the maximum N / 2 dimensions (717 dimensions in this example) of the same format. Deviation vector δV (j).

[Formula 5]
RE = {δVq (j) · δVd (j)} / {| δVq (j) || δVd (j) |} = Σ _{j = f1,...,} F2 {(δVq (j)) × (δVd (j) )} / [{Σ _{j = f1,...,} F2 (δVq (j)) ² } ^1/2 × {Σ _{j = f1,...,} F2 (δVd (j)) ² } ^1/2 ]

  Any correlation value may be used as long as the correlation between the registered feature data δVd (j) and the feature data δVq (j) can be evaluated. In the present embodiment, both are deviation vectors. The inner product of both is calculated as a correlation value RE. As shown in the above [Equation 5], in this embodiment, the inner product is divided by the magnitude of each vector (the square sum of squares of δVq (j) and δVd (j)) to calculate the correlation value RE. The difference in the volume of sound that is the target of both feature data is removed.

  When the correlation value RE is obtained for one registered feature data, it is determined whether the processing has been completed for the registered feature data corresponding to all sound sources in the sound source database 50 (S232). The correlation calculation is repeatedly performed until a correlation value RE is obtained for (S231).

  When the correlation value RE is obtained for all sound sources, the sound source having the maximum correlation value RE is specified. At this time, a threshold value is set for the correlation value RE, and only those having a correlation value RE larger than the threshold value are selected. Therefore, when the maximum correlation value RE is equal to or less than the threshold value, no sound source is specified. In that case, it means that there is no sound similar to the sound that has been emitted in the sound source database 50, and the sound source identification fails.

  If the maximum correlation value RE is larger than the threshold value, the sound source is specified by the sound source identification information recorded in the sound source database 50 in association with the registered feature data for which the correlation value RE is calculated. (S233). When the sound source is specified, the sound source information output means 90 outputs the specified sound source identification information in a predetermined format (S234). The output can be in various formats such as a display output of content related to the sound source identification information, a data transmission output of the sound source identification information to other information devices, and is output by a device corresponding to the set format. For example, by storing the musical instrument details, commentary content, and commentary content program in association with the sound source identification information in a separately prepared multimedia content database, the commentary content is activated, and the display output I / F 6a is set. Displayed on the display device. Alternatively, by transmitting sound source identification information to another information terminal via a USB interface or a wireless interface, it is possible to cause the information terminal to start a predetermined program corresponding to the sound source identification information. . In this embodiment, as shown in S234 of FIG. 9, the sound source identification information is output every time a sound source is specified. Therefore, in this embodiment, every time a sound source is specified, the information device outputs the sound source identification information to other information devices, etc., and after the failure to specify the sound source in S240 of FIG. Collectively, output based on a plurality of sound source identification information is performed. The present invention is not limited to this embodiment, and it is also possible to output sound source identification information collectively to other information devices after all sound sources are specified.

  When outputting sound source identification information, if the information is output to an information processing apparatus such as a computer, the information processing apparatus can perform processing according to the sound source identification information. For example, by installing a program for performing different processing for each sound source identification information on a computer, different processing is performed only by directing a microphone toward a different sound source.

(3. Details of feature data correction)
Next, the correction of feature data in S250 will be described. First, the feature data correcting unit 31 sets the registration-side average spectrum Vd (j) recorded in association with the specified sound source as Vdmax (j), and Vdmax (j) (j = f1, f1 + 1,... Among the f2-1 and f2), j that maximizes the value of Vdmax (j) is specified, and j at that time is defined as jmax. Then, the feature data correction unit 31 executes the processing according to the following [Equation 6], whereby the search side average spectrum Vq (j) of j = f1, f1 + 1,. A corrected average spectrum Vq ′ (j) obtained by correcting each value is calculated. It should be noted that an embodiment in which the registration-side average spectrum V (j) is not registered in the sound source database 50 is possible, but in that case, the spectrum is associated with the feature data δV (j) in the sound source database 50. The average value Av is registered. Then, the feature data correction unit 31 executes a process according to a formula V (j) = [δV (j) + Av] / j obtained by modifying the above [Formula 4], and calculates a registration-side average spectrum Vd (j). Perform the process.

[Formula 6]
Vq ′ (j) = Vq (j) −Vdmax (j) · Vq (jmax) / Vdmax (jmax)
If Vq '(j) <0, set Vq' (j) = 0

  In the above [Equation 6], Vq (jmax) and Vdmax (jmax) are both fixed values because they are fixed at j = jmax. Therefore, the operator “·” between Vq (jmax) / Vdmax (jmax) and the vector Vdmax (j) means multiplication. As shown in [Formula 6] above, the corrected average spectrum Vq ′ (j) specifies the search side Vq (jmax) for the frequency number jmax that maximizes the array Vdmax (j) corresponding to the specified sound source. It is calculated by dividing by the calculated Vdmax (jmax), and multiplying the value by the array Vdmax (j) and subtracting it from the search side average spectrum Vq (j). That is, by multiplying the array Vdmax (j) corresponding to the identified sound source by the ratio of the frequency jmax of the maximum component of the identified sound source to the search side and subtracting it from the search side average spectrum Vq (j), the original A corrected average spectrum Vq ′ (j) obtained by removing the contribution of each value of the spectrum corresponding to the specified sound source is obtained from each value of the spectrum on the search side. When Vq ′ (j) becomes less than 0, it is forcibly set to “0” because a negative value as Vq ′ (j) is physically impossible.

  Then, the feature data correcting unit 31 executes the processing according to the following [Equation 7] using the corrected average spectrum Vq ′ (j), thereby obtaining the corrected feature data δVq ′ (j) which is a deviation vector. calculate.

[Formula 7]
Av ′ = Σ _{j = f1,...,} F2 Vq ′ (j) · j / (f2−f1 + 1)
δVq ′ (j) = Vq ′ (j) · j−Av ′

  The first and second formulas of [Formula 7] are obtained by replacing V (j) with Vq ′ (j) and Av with Av ′ in [Formula 3] and [Formula 4], respectively. Using the corrected feature data δVq ′ (j) calculated according to the above [Equation 7], the sound source search process of S230 is performed, and the second main sound source is specified. When the second main sound source is successfully identified, the corrected average spectrum Vq ′ (j) is set as a new search-side average spectrum Vq (j), and a new correction is made in S250 using the above [Expression 6] and [Expression 7]. Characteristic data δVq ′ (j) is obtained. Until the identification of the sound source fails, the processing of S230 to S250 is repeatedly executed by the feature data correction unit 31 and the sound source database search unit 80.

  As shown in [Formula 6] above, for the frequency number jmax that maximizes the registration-side average spectrum corresponding to the identified sound source, the corresponding search-side average spectrum component Vq (jmax) is specified. The search-side average spectrum is corrected by dividing the spectrum component Vdmax (jmax) by the value obtained by multiplying the value by the registration-side average spectrum Vdmax (j) from the search-side average spectrum Vq (j). Since the data has been corrected, it is possible to remove the portion corresponding to the specified registration-side average spectrum of the sound source from the search-side average spectrum of the acquired acoustic signal, and the corrected search-side average spectrum is specified. Since the sound source excluding the generated first sound source is expressed, it becomes possible to accurately specify the second and subsequent sounds.

(4. Modification of feature data correction)
Next, a modified example of the feature data correction in S250 will be described. In the above example, the search-side average spectrum Vq (j) that is the basis of the search feature data δVq (j) is corrected, and then the corrected feature data δVq ′ (j) is obtained. Assuming that the data δVq (j) is represented by a linear combination of registered feature data δVd (j) corresponding to a plurality of types of sound sources, the level of the most important sound source is estimated, and the second and subsequent main sound sources are selected. Judge with high accuracy. However, in order to simplify the description, the sound source to be searched is limited to two sound sources.

  In the modification, it is assumed that the search feature data δVq (j) can be expressed by the following [Equation 8] when the level of the most main sound source is V1, and the level of the second main sound source is V2. In [Formula 8], the operator “·” on the right side means an operation of scalar values V1 and V2 and a vector, and means multiplication.

[Formula 8]
δVq (j) = V1 · δVdmax (j) + V2 · δVd2max (j)

  The inner product of the search feature data δVq (j) and the registered feature data δVdmax (j) of the most main sound source is expressed by the following [Equation 9]. In [Equation 9], the operator “•” on the left side and the operator “•” in {} on the right side are operations between vectors, and therefore mean an inner product, and the operator “• outside the {} on the right side. "" Means a multiplication because it is an operation with a scalar value.

[Formula 9]
δVq (j) · δVdmax (j) = V1 · | δVdmax (j) | ² + V2 · {δVd2max (j) · δVdmax (j)}

  Here, if the registered feature data δVd2max (j) of the second main sound source is significantly different from the registered feature data δVdmax (j) of the most main sound source, it can be assumed to be linearly independent. Since the inner product value {δVd2max (j) · δVdmax (j)} of the second term can be approximated to 0, the level V1 of the most important sound source is expressed by the following [Equation 10].

[Formula 10]
V1 = {δVq (j) · δVdmax (j)} / | δVdmax (j) | ²

  Therefore, the feature data correcting unit 31 executes the process according to the above [Equation 10] and calculates the level V1 of the most main sound source. Further, the feature data correcting unit 31 calculates the corrected feature data δVq ′ (j), which is a deviation vector, by executing processing according to the following [Equation 11] using the level V1 of the main sound source. .

[Formula 11]
δVq ′ (j) = δVq (j) −V1 · δVdmax (j)

  According to the correction of the feature data according to the modified example, as shown in [Formula 10], the correlation value between the registered feature data δVdmax (j) corresponding to the identified sound source and the search feature data δVq (j) is used as the sound source. And the corrected feature is obtained by subtracting, from the search feature data δVq (j), the value obtained by multiplying the specified registered feature data δVdmax (j) by the correlation value V1 as shown in [Formula 11]. Since the data δVq ′ (j) is generated, correction feature data excluding the feature of the first sound source can be generated quantitatively from the search feature data, and the second and subsequent sound sources can be specified with high accuracy. become. By using the corrected feature data δVq ′ (j) to calculate the inner product with the registered feature data δVd2max (j) of the second main sound source, the level V2 of the second main sound source can be calculated. That is, [Formula 9] and [Formula 10] can be calculated by modifying them into [Formula 9a] and [Formula 10a] below.

[Formula 9a]
δVq ′ (j) · δVd2max (j) = V2 · | δVd2max (j) | ²

[Formula 10a]
V2 = {δVq ′ (j) · δVd2max (j)} / | δVd2max (j) | ²

(5. Modified examples of window functions)
In the above embodiment, feature data based on an average spectrum in the time series direction is generated for the sound source. However, generally, as shown in FIG. 10 (a), the spectrum of a sound dynamically changes in four phases of a rising part (attack and decay), a steady part (sustain), and a falling part (release) of the sound. It is not appropriate to express the feature based on a single average spectrum. For example, a reverse waveform obtained by reversing the time axis of a certain acoustic signal does not change the average spectrum, but as shown in FIGS. 10B and 10C, since the waveform is opposite in the time axis direction, It changes dramatically.

  In the above embodiment, when performing Fourier transform, the window function W (i), which is a Hanning window, is uniformly used for all acoustic frames. However, the Hanning window has a bilaterally symmetric shape. There is a problem that the turning waveform cannot be identified. The reverse rotation waveform is obtained by reversing the time axis of a certain acoustic signal. For example, when an acoustic signal of a piano tone is reproduced from the opposite direction, an organ-like tone color is obtained. It cannot be identified.

  In order to be able to deal with such a reverse rotation waveform, in the present invention, two window functions W (1, i) that are asymmetrical left and right are used instead of the window function W (i) as shown in FIG. , W (2, i) can also be used. As shown in FIG. 11B, the window function W (1, i) is set to take the maximum value 1 at the position of the predetermined sample number i and take the minimum value 0 at the rear part. Which sample number has the maximum value depends on the design of the window function W (1, i), but in the present embodiment, it is defined by the following [Equation 12]. Further, as shown in FIG. 11C, the window function W (2, i) is set to take the maximum value 1 at the position of the predetermined sample number i and take the minimum value 0 at the front part. Has been. Which sample number takes the maximum value depends on the design of the window function W (2, i), but is defined by the following [Equation 13] in this embodiment. These window functions are known window functions as disclosed in Patent Document 3.

[Formula 12]
When i ≦ N / 8, W (1, i) = 0.0
When N / 8 <i ≦ 3N / 8, W (1, i) = 0.5−0.5 cos (4π (i−N / 8) / N)
When 3N / 8 <i ≦ 11N / 16, W (1, i) = 1.0
When 11N / 16 <i ≦ 13N / 16, W (1, i) = 0.5 + 0.5 cos (8π (i-11N / 16) / N)
When i> 13N / 16, W (1, i) = 0.0

[Formula 13]
When i ≦ 3N / 16, W (2, i) = 0.0
When 3N / 16 <i ≦ 5N / 16, W (2, i) = 0.5−0.5 cos (8π (i−3N / 16) / N)
When 5N / 16 <i ≦ 5N / 8, W (2, i) = 1.0
When 5N / 8 <i ≦ 7N / 8, W (2, i) = 0.5 + 0.5 cos (4π (i−5N / 8) / N)
When i> 7N / 8, W (2, i) = 0.0

  As described above, by using two window functions that are asymmetrical to the left and right, it is possible to distinguish between a normal reproduction case and a reverse rotation waveform. However, the reverse rotation waveform is used less frequently due to quality problems, and the waveform of the actual piano tone and organ tone is not a simple shape in which the time axes are mutually inverted, so that the above W (i) Even if a target object uses a window function, it is possible to identify a normal sound source.

  The preferred embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments, and various modifications can be made. For example, in the above embodiment, as the feature data, a deviation vector obtained by subtracting the average value from the product of each frequency multiplied by the weight is calculated. However, a vector in a state where the average value is not reduced may be used as the feature data.

1, 1a ... CPU
2, 2a ... RAM
3, 3a ... Storage device 4, 4a ... Key input I / F
5, 5a ... Data I / O I / F
6, 6a: Display output I / F
7a ... Voice input I / F
DESCRIPTION OF SYMBOLS 10 ... Silent section deletion means 20 ... Acoustic frame reading means 30 ... Feature data generation means 31 ... Feature data correction means 40 ... Feature data registration means 50 ... Sound source database 60 ... Microphone 70 ... Acoustic signal acquisition means 80 ... Sound source database search means 90 ... Sound source information output means

Claims (11)

A device for acquiring sounds emitted from a plurality of sound sources and identifying each sound source,
For each sound source, registered feature data that represents the feature, a sound source database in which identification information for identifying each sound source is registered in association with each other,
Sound signal acquisition means for recording the sound emitted from the plurality of sound sources and acquiring it as a digital sound signal;
Frequency analysis for the acoustic signal, and feature data generating means for generating search feature data based on a search-side average spectrum that is a spectrum averaged over time,
A correlation calculation is performed with each of the generated search feature data and each of the registered feature data registered in the sound source database, and the correlation value obtained has a maximum correlation value, and the correlation value is a predetermined value. Sound source database search means for identifying a sound source corresponding to registered feature data satisfying a threshold value or more by the identification information;
Using the registered feature data corresponding to the sound source specified by the sound source database search means, correcting the search feature data, and generating feature data correction means,
The sound source database search means executes the process of specifying the sound source using the generated correction feature data and the registered feature data registered in the sound source database excluding the already specified sound source,
The feature data correction means uses the registered feature data registered in the sound source database in association with the sound source specified by the sound source database search means, and serves as a basis for creating the registered feature data. Create a registration-side average spectrum that is an averaged spectrum, and for the frequency number that gives the largest component of the created registration-side average spectrum, the corresponding value of the search-side average spectrum is the largest component of the registration-side average spectrum. And the value obtained by multiplying each frequency number including the frequency number giving the maximum component by the registration-side average spectrum corresponding to the specified sound source is subtracted from the search-side average spectrum. To correct the search-side average spectrum corresponding to each frequency number,
The sound source database search unit and the feature data correction unit repeatedly perform a process until a predetermined condition is satisfied. A device for acquiring sounds emitted from a plurality of sound sources and identifying each sound source,
For each sound source, registered feature data that represents the feature, a sound source database in which identification information for identifying each sound source is registered in association with each other,
Sound signal acquisition means for recording the sound emitted from the plurality of sound sources and acquiring it as a digital sound signal;
Based on a value obtained by performing frequency analysis on the acoustic signal and multiplying each frequency component of the search-side average spectrum , which is a spectrum averaged over time, by a weight that increases as the frequency value increases. Feature data generating means for generating search feature data;
A correlation calculation is performed with each of the generated search feature data and each of the registered feature data registered in the sound source database, and the correlation value obtained has a maximum correlation value, and the correlation value is a predetermined value. Sound source database search means for identifying a sound source corresponding to registered feature data satisfying a threshold value or more by the identification information;
Using the registered feature data corresponding to the sound source specified by the sound source database search means, correcting the search feature data, and generating feature data correction means,
The sound source database search means executes the process of specifying the sound source using the generated correction feature data and the registered feature data registered in the sound source database excluding the already specified sound source,
The sound source database search unit and the feature data correction unit repeatedly perform a process until a predetermined condition is satisfied. In claim 2 ,
The feature data correction unit obtains a correlation value between the registered feature data corresponding to the sound source specified by the sound source database search unit and the search feature data, and multiplies the specified registered feature data by the correlation value The correction feature data is generated by subtracting the search feature data from the search feature data. Oite to claim 1,
The feature data generation means generates the search feature data based on a value obtained by multiplying each frequency component of the search-side average spectrum by a weight that increases as the frequency value increases. A device for identifying a plurality of sound sources as a feature. In any one of Claims 2-4 ,
The feature data generation means further includes a value obtained by multiplying each frequency component by a weight that increases as the frequency value increases as the search feature data. An apparatus for identifying a plurality of sound sources, wherein a deviation vector is generated by subtracting an average value obtained by multiplying a weight that increases. In any one of Claims 1-5,
The feature data generation means specifies a silent section in which the amplitude of the acoustic signal acquired by the acoustic signal acquisition means is less than a predetermined value and continues for a predetermined time or longer, deletes the specified silent section and deletes the specified acoustic section. A plurality of sound source identification devices, wherein correction for reducing the time is performed, and the frequency analysis is performed on the corrected acoustic signal. In any one of Claims 1-6,
The feature data generation means, as the frequency analysis, divides the acoustic signal into a predetermined section unit, and waveform data obtained by superimposing a weight function defined in advance on the same section length on the divided waveform data The search side average spectrum is obtained by performing a Fourier transform on each and averaging the spectrum obtained by taking the mean square value of the real part and the imaginary part for each section and averaging it over all the sections. A multiple sound source identification device. In claim 7,
Each of the sections overlaps with the adjacent section by a time width of ½ of the section length,
Two types of weighting functions are defined in an asymmetric shape with respect to the time axis direction. One weighting function is superimposed on an odd-numbered section, and the other weighting is performed on an even-numbered section. An apparatus for identifying a plurality of sound sources, wherein a function is superimposed. A device using the identification information specified by the multiple sound source identification device according to any one of claims 1 to 8 , wherein a program for performing different processing for each identification information is stored, the corresponding identification information output from the plurality tone identification device activating the program, the information processing apparatus interlocked with the plurality of sound sources, characterized that you execute processing according to the program.   A program for causing a computer to function as the multiple sound source identification device according to any one of claims 1 to 8.   A program for causing a computer to function as an information processing apparatus linked to a plurality of sound sources according to claim 9.

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