JP2016194657A - Sound source separation device, sound source separation method, and sound source separation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To effectively separate a sound source even in a distributed microphone array environment.SOLUTION: A process for estimating sound source existence posterior probability pertaining to each sound source in each microphone node on the basis of a microphone node observation signal and correction information, and a process for modeling the co-occurrence relation of sound source existence posterior probability, estimating a parameter in a model in which the co-occurrence relation of sound source existence posterior probability becomes large, and calculating correction information for correcting the sound source existence posterior probability on the basis of the parameter are repeated until an output value converges, and finally, the microphone node observation signal is filtered with the sound source existence posterior probability or updated information, whereby a sound source signal is estimated.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a sound source separation device, a sound source separation method, and a sound source separation program.

  When an acoustic signal is collected in an environment where a plurality of target sound sources exist, a mixed signal in which the target signals overlap each other is often observed. At this time, when the target sound source of interest is an audio signal, the clarity of the target sound is greatly reduced due to the influence of other sound source signals superimposed on the target signal.

  In addition, when other sound source signals are superposed on the target speech signal (hereinafter referred to as the target signal), it becomes difficult to accurately extract the nature of the target signal from the observed signal, and automatic speech recognition (hereinafter referred to as The recognition rate of the speech recognition system is also significantly reduced. Therefore, in order to prevent the recognition rate from decreasing, it is necessary to devise a method (method) for separating a plurality of target signals and restoring the clarity of the target signals.

  The elemental technology for separating a plurality of target signals can be used for various acoustic signal processing systems. For example, a hearing aid that extracts the target signal from the sound collected in the real environment to improve ease of hearing, a TV conference system that improves the intelligibility of the voice by extracting the target signal, and audio used in the real environment It can be used in a recognition system and a machine-human interaction device in a machine control interface.

  FIG. 7 shows a functional configuration of a conventional sound source separation device (for example, see Non-Patent Document 1), and its operation will be briefly described. FIG. 7 is a diagram showing a conventional sound source separation device. As shown in FIG. 7, the sound source separation device 50 includes a sound source existence posterior probability estimation unit 51 and a filtering unit 52 for all microphones.

  The sound source existence posterior probability estimation unit 51 for all microphones receives a sound source signal emitted from a plurality of sound sources by a plurality of microphones as input, and a feature vector characterizing each time frequency bin of each observation signal And categorizing the feature vectors to calculate the sound source existence posterior probability that is the existence probability for each sound source. The filtering unit 52 recovers the sound source signal by multiplying the observation signals of a plurality of channels collected by a plurality of microphones by the existence probability.

H. Sawada, S. Araki, and S. Makino, “Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment,” IEEE Trans. Audio, Speech and Lang. Process., Vol. 19, pp.516- 527, March 2011.

  However, in the conventional sound source separation technology, it is assumed that all microphones are densely arranged, and a situation where the microphones are spatially distributed (hereinafter referred to as a distributed microphone array environment) is not assumed. It was. That is, if a plurality of microphone nodes are arranged in a spatially dispersed manner, the sound pressure of a certain sound source observed at each microphone node does not become comparable. In extreme cases, a situation may occur where a sound source is substantially unobservable at a microphone node. In such a situation, it is appropriate to assume different sound source existence probabilities (activity patterns) at each microphone node. Note that the microphone node refers to a microphone array including two or more microphones. For example, an IC recorder having a plurality of microphones corresponds to one microphone node.

  However, in the conventional method, if all observations obtained at all microphone nodes at the recording site are used, the sound source existence probability common to all microphone nodes can only be calculated. Moreover, if the conventional method is applied independently for each microphone node, the sound source existence probability can be calculated for each microphone node. In this case, however, it is beneficial to exist between each microphone node. As a result, there is a problem that effective sound source separation cannot be performed in a distributed microphone array environment.

  The present invention has been made in view of such problems, and an object thereof is to perform sound source separation effectively even in a distributed microphone array environment.

  The sound source separation device according to the present invention calculates a sound source existence posterior probability for each sound source at each microphone node based on a microphone node observation signal that is a multi-channel observation signal obtained by collecting sound source signals emitted from a plurality of sound sources by a plurality of microphones. A sound source presence posterior probability estimation unit for each microphone node that updates and updates the sound source presence posterior probability based on update information that is information for updating the sound source presence posterior probability; Assuming that the sound source existence posterior probability of each sound source between microphone nodes co-occurs, the co-occurrence relationship of the sound source existence posterior probability is modeled, and the co-occurrence of the sound source existence posterior probability of each microphone node is large. The parameters in the model are estimated so that the update information is calculated based on the parameters. The sound source presence posterior probability co-occurrence pattern detection unit between the microphone nodes, the update of the sound source presence posterior probability in the sound source existence posterior probability estimation unit for each microphone node, and the update in the sound source presence posterior probability co-occurrence pattern detection unit between the microphone nodes By filtering the calculation of information using the sound source existence posterior probability or the update information, with respect to the convergence determination unit that repeatedly executes the sound source posterior probability or until the parameter converges, and the microphone node observation signal, An output sound estimator for estimating the sound source signal of each sound source.

  The sound source separation method of the present invention is a sound source separation method executed by a sound source separation device, and is a microphone node observation that is a multi-channel observation signal obtained by collecting sound source signals emitted from a plurality of sound sources by a plurality of microphones. For each microphone node that estimates the sound source presence posterior probability for each sound source in each microphone node based on the signal, and updates the sound source presence posterior probability based on update information that is information for updating the sound source presence posterior probability Assuming that the sound source presence posterior probability of each sound source between the microphone nodes in the same time frequency bin in the sound source existence posterior probability estimation step, models the co-occurrence relationship of the sound source presence posterior probability, The parameters in the model are set so that the co-occurrence of the sound source presence posterior probability of each microphone node increases. A sound source existence posterior probability co-occurrence pattern detecting step for calculating the update information based on the parameter, and updating the sound source presence posterior probability in the sound source presence posterior probability estimating step for each microphone node; For the microphone node observation signal, a convergence determination step that repeatedly executes the calculation of the update information in the sound source presence posterior probability co-occurrence pattern detection step between the microphone nodes until the sound source presence posterior probability or the parameter converges, And an output sound estimation step of estimating the sound source signal of each sound source by filtering using a sound source posterior probability or the update information.

  According to the present invention, sound source separation can be performed effectively even in a distributed microphone array environment.

FIG. 1 is a diagram illustrating an outline of a configuration of a sound source separation device according to an embodiment. FIG. 2 is a block diagram illustrating a detailed configuration of the sound source separation device according to the embodiment. FIG. 3 is a diagram illustrating an acoustic environment in which the sound source separation device according to the embodiment is used. FIG. 4 is a diagram illustrating the sound source separation performance of the sound source separation device according to the embodiment. FIG. 5 is a flowchart illustrating processing of the sound source separation device according to the embodiment. FIG. 6 is a diagram illustrating a computer that executes a sound source separation program. FIG. 7 is a diagram showing a conventional sound source separation device.

  Embodiments of a sound source separation device, a sound source separation method, and a sound source separation program according to the present application will be described below in detail with reference to the drawings. Note that the sound source separation device, the sound source separation method, and the sound source separation program according to the present application are not limited by this embodiment. First, observation signal modeling will be described.

[Modeling of observed signals]
In modeling observation signals, variables are first defined. I is the total number of microphone nodes, J is the number of clusters in each microphone node, K is the number of sound sources (J = K in this specification, but J and K may be different values), x _i Is the observed feature value of the i-th microphone node, x is a set of x _i that summarizes the observed feature values of all microphone nodes, n _{i, j} is the activity of the sound source corresponding to the j-th cluster of the i-th microphone node Binary variable to represent (1 means the sound source is active, 0 means the sound source is not active), n is a set of n _{i, j} , a _k is the potential sound source activity common to all microphone nodes A variable to represent (in the case of 1, the sound source is active, in the case of 0, the sound source is not active), and a represents a set of a _k .

In addition, since all the processes in the following description are performed independently for each frequency bin, the frequency index is omitted for simplicity. When conventional clustering-based sound source separation (see, for example, Non-Patent Document 1) is applied to the i-th microphone node observation signal x _i (x _i corresponds to the normalized observation vector), the microphone node observation signal x _i It was expressed by a mixed distribution type probability model as shown in (1).

At this time, p (n _{i, j} ) in Expression (1) represents the prior probability that the j-th sound source is active at the i-th node. In the equation (1), p (x _i | n _{i, j} ; θ ⁽ⁿ _i ⁾ ) represents a distribution such as a Watson distribution, and θ ⁽ⁿ _i ⁾ represents a distribution parameter (an average direction parameter in the case of Watson distribution ^). Corresponds to the density parameter, and in the case of Gaussian distribution, it corresponds to the mean, variance, etc.). The p (n _{i, j} | x _i ) obtained after adjusting the distribution parameters to maximize the likelihood represented by this formula is obtained when information obtained from microphone nodes other than the i-th is not used. This is the sound source existence posterior probability for the j th sound source at the i th node that can be obtained.

  On the other hand, in the embodiment, the probability model of the observation signal x (that is, the likelihood p (x; θ) related to the observation signal) is expressed as in Expression (2).

The third stage of Equation (2) is obtained on the assumption that the observation values x _i of each microphone node are independent. Looking at equation (2), the present invention shows a new potential common to all microphone nodes in the part of the prior probability indicating the activity of the sound source (ie, p (n, a; θ ^(w) )). It can be seen that a, which is a variable representing the sound source activity, is added, and the prior probability is represented by the joint probability of the sound source activity information n at each node and the potential sound source activity information a common to all nodes.

Prior probabilities p (n, a; θ ^(w) ) indicating sound source activity can take various forms, but here the co-occurrence of sound source activity between microphone nodes (ie, n _{1, j} , n _{2, j} , ..., n _{I, j} co-occurrence) (Restricted Boltzman Machine (RBM) as shown in equations (3) to (5)) In the form of

Θ ^{(w) in} Equation (3) represents parameters {W _i , b _i , c} used in the RBM. A restricted Boltzmann machine is a model that can capture the co-occurrence of observation signals between nodes (corresponding to sound source existence posterior probabilities between nodes in the embodiment), such as being used in a collaborative filter. Posterior probability for the value a _k of at the hidden layer when input n to common input layer is given in RBM, also post for the value n in the input layer when given the value a _k of at the hidden layer Define the probability and use it in the parameter estimation algorithm. Those posterior probabilities are defined as in equations (6) to (8).

[Concept of the present invention]
Prior to detailed description of the embodiments, an outline of the idea of the present invention will be described. The present invention calculates a sound source presence posterior probability that is a filter for sound source separation in each microphone node. In the conventional method, information from other microphone nodes cannot be taken in to calculate this value.

  However, in the proposed method, if a sound source activity pattern that exchanges information between microphone nodes and co-occurs with a sound source activity observed at a certain microphone node is observed at other microphone nodes, their co-occurrence The parameter estimation proceeds at the sound source presence posterior probability co-occurrence pattern detection unit 12 between the microphone nodes. As a result, if a certain sound source is observed at a plurality of microphone nodes, the parameters of the existence posterior probabilities related to the sound source are adjusted so as to increase the co-occurrence with each other, and more accurate estimation becomes possible.

  For example, it is assumed that posterior probabilities relating to a certain sound source often co-occur in the same time frequency bin of the microphone nodes 1, 2, and 3. Under such circumstances, in a certain time frequency bin, it is assumed that the co-occurrence relationship is confirmed only with the microphone nodes 1 and 2 with respect to the sound source, and the microphone node 3 does not co-occur. Then, there is a high probability that the estimated value in the time frequency bin of the microphone node 3 is an error.

  Such an error is eliminated by estimating the parameters so as to enhance the co-occurrence of existence posterior probabilities related to this sound source at the microphone nodes 1, 2, and 3. Conversely, if the same sound source is active only at the microphone node 1 and is not active at the microphone nodes 2 and 3, it is highly likely that the same sound source is not active at that time frequency bin. In such a case as well, the co-occurrence of “not active” increases, so that the error of the microphone node 1 is corrected. A specific procedure for learning parameters so as to increase the co-occurrence of sound source presence posterior probabilities between microphone nodes will be described in detail in the description of the embodiment.

[Embodiment]
The configuration of the sound source separation device according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an outline of a configuration of a sound source separation device according to an embodiment. The sound source separation device 10 includes a sound source presence posterior probability estimation unit 11 for each microphone node, a sound source presence posterior probability co-occurrence pattern detection unit 12 between microphone nodes, a convergence determination unit 13, and an output sound estimation unit 14.

  As shown in FIG. 1, the sound source separation device 10 receives a plurality of channels of observation signals obtained by collecting sound source signals emitted from a plurality of sound sources with a plurality of microphones. In particular, when the processing in the sound source separation device 10 is described, the input observation signals of a plurality of channels may be referred to as microphone node observation signals.

  The sound source existence posterior probability estimation unit 11 for each microphone node is a sound source related to each sound source in each microphone node based on a microphone node observation signal which is a multi-channel observation signal obtained by collecting sound source signals emitted from a plurality of sound sources by a plurality of microphones. The existence posterior probability is estimated, and the sound source existence posterior probability is updated based on update information that is information for updating the sound source existence posterior probability.

For example, the microphone node-specific sound source existence posterior probability estimation unit 11 is based on an observation feature value x _i obtained by collecting sound source signals emitted from a plurality of sound sources in a time frame t, which is a microphone node observation signal, at an i-th microphone node. By estimating p (x _i | n _i ; θ ⁽ⁿ _i ⁾ ) in Equation (2), the existence posterior probability p (n _{i, j} | x _i ) and re-estimate p (x _i | n _i ; θ ⁽ⁿ _i ⁾ ) based on the update information so that the likelihood of the observed signal p (x; θ) is maximized To update the existence a posteriori probability p (n _{i, j} | x _i ) at the i th microphone node of the j th sound source, which is the sound source existence a posteriori probability.

  The sound source presence posterior probability co-occurrence pattern detection unit 12 between the microphone nodes assumes that the sound source presence posterior probability of each sound source between the microphone nodes co-occurs in the same time frequency bin, and determines the co-occurrence relationship of the sound source presence posterior probabilities. Modeling is performed, parameters in the model are estimated so that the co-occurrence of the sound source existence posterior probability of each microphone node is increased, and update information is calculated based on the parameters.

For example, the inter-microphone sound source existence posterior probability co-occurrence pattern detection unit 12 generates p (n _{i, j} | x _{i, i} for all time frames t of sound source existence posterior probabilities for all i, all j, and all t _{. t} ), the prior probability p (n, a; θ indicating the activity of the sound source, which is the joint probability of the sound source activity information n at each microphone node and the potential sound source activity information a common to all nodes. ^(w) ) is estimated so that θ ^(w) , which is a parameter of the model indicating the co-occurrence of the sound source posterior probability, is calculated so that update information is calculated.

  The convergence determination unit 13 updates the sound source presence posterior probability in the sound source presence posterior probability estimation unit 11 by microphone node and calculates update information in the sound source presence posterior probability co-occurrence pattern detection unit 12 between the microphone nodes. Is executed repeatedly until.

  The output sound estimation unit 14 estimates the sound source signal of each sound source by filtering the microphone node observation signal using the sound source existence posterior probability or the update information.

  Next, each part of the sound source separation device 10 will be described in detail with reference to FIG. FIG. 2 is a block diagram illustrating a detailed configuration of the sound source separation device according to the embodiment. The sound source separation device 10 receives microphone node observation signals from a plurality of microphone nodes 20, estimates a sound source image of each sound source, and outputs it to the output device 21 and the like. The sound source separation device 10 may output the estimated sound source image to an output device such as a speaker, or may output and store it in a storage device or the like.

  As illustrated in FIG. 2, the sound source separation device 10 includes a microphone node-specific sound source presence posterior probability estimation unit 11, an inter-microphone sound source presence posterior probability co-occurrence pattern detection unit 12, a convergence determination unit 13, and an output sound estimation unit. 14, an input unit 15, and an output unit 16. The sound source presence posterior probability estimation unit 11 by microphone node includes a first sound source presence posterior probability initial value calculation unit 111 and a first sound source presence posterior probability update unit 112. The microphone node sound source presence posterior probability co-occurrence pattern detection unit 12 includes a co-occurrence relation model parameter calculation unit 121 and a second sound source presence posterior probability calculation unit 122.

  First, an observation signal obtained by collecting sound source signals emitted from a plurality of sound sources by a plurality of microphone nodes 20 is input to the input unit 15. Then, the first sound source presence posterior probability initial value calculation unit 111 is obtained by using information obtained from each microphone node using observation signals obtained by collecting sound source signals emitted from a plurality of sound sources at a plurality of microphone nodes. A first sound source existence posterior probability, which is a probability that each sound source is active, is calculated.

  Next, the co-occurrence relationship model parameter calculation unit 121 models the co-occurrence relationship between the first sound source existence posterior probabilities of each microphone node, calculates the parameters of the model so that the co-occurrence relationship becomes large, and has already calculated it. If parameters exist, update to the latest parameters.

  Furthermore, the second sound source presence posterior probability calculation unit 122 calculates a second sound source presence posterior probability, which is a sound source presence posterior probability using information obtained from a plurality of microphone nodes, using parameters. Then, the first sound source presence posterior probability update unit 112 updates the first sound source presence posterior probability using the second sound source presence posterior probability.

  Here, the convergence determination unit 13 determines whether or not the update amount in the first sound source presence posterior probability update unit 112 and the co-occurrence relation model parameter calculation unit 121 is equal to or less than a predetermined threshold, and the update amount is predetermined. If it is not less than the threshold value, the processes in the first sound source presence posterior probability update unit 112 and the co-occurrence relation model parameter calculation unit 121 are repeatedly executed until the update amount becomes equal to or less than the predetermined threshold value.

  Finally, the output sound estimation unit 141 performs filtering on the observation signal using the second sound source existence posterior probability when the convergence determination unit 13 determines that the update amount is equal to or less than a predetermined threshold. Estimate the sound source image for each sound source. Hereinafter, processing in each unit will be described.

[Processing by microphone node-specific sound source existence posterior probability estimation unit 11 (calculation of initial values)]
First, the processing in the microphone node-specific sound source presence posterior probability estimation unit 11 before processing by the microphone node sound source presence posterior probability co-occurrence pattern detection unit 12 will be described. At this time, the microphone node-specific sound source presence posterior probability estimation unit 11 calculates an initial value of the first sound source presence posterior probability. In addition, the update process of the 1st sound source presence posterior probability using the correction information output from the sound source presence posterior probability co-occurrence pattern detection unit 12 will be described later.

First, the first sound source existence posterior probability initial value calculation unit 111 of the sound source existence posterior probability estimation unit 11 by microphone node collects sound source signals emitted from a plurality of sound sources at the i-th microphone node, and is an observed feature amount x _i. And the existence posterior probability p (n _{i, j} | x _i ) at the i-th microphone node of the j-th sound source is calculated using the equation (1). Specifically, the first sound source presence posterior probability initial value calculation unit 111 calculates the initial value by estimating the distribution parameter θ ⁽ⁿ _i ⁾ by maximum likelihood estimation so as to maximize the value of Equation (1). To do. It is known that the maximum likelihood estimation of the mixture distribution parameter in Equation (1) can be performed using an expectation maximization algorithm, in which p (n _{i, j} | x _i ) is calculated. The

[Processing at the sound source existence posterior probability co-occurrence pattern detection unit 12 between microphone nodes]
Next, the co-occurrence relation model parameter calculation unit 121 of the sound source existence posterior probability co-occurrence pattern detection unit 12 between the microphone nodes performs a set of the first sound source existence posterior probabilities obtained above, that is, all i (microphone node indexes). , All j (cluster index at each microphone node), and p (n _{i, j} | x _{j, t} ) (adding time frame index t to x) for all time frames t Model (learn) the relationship. Specifically, the co-occurrence relationship model parameter calculation unit 121 maximizes the RBM parameters {W _i , b _i , c} represented by the equation (4) or the like, p (n, a; θ ^(w) ). To learn. This learning is generally performed by the steepest descent method using contrastive divergence (Reference 1: GE Hinton, “A practical guide to training restricted Boltzmann machines,” Univ. Of Toronto, Toronto, ON, Canada, Tech. Rep. ., 2010.) is used. Here, the gradient of each parameter {W _i , b _i , c} estimated by the steepest descent method is calculated by equations (9) to (11).

  Then, after calculating each gradient, the co-occurrence relation model parameter calculation unit 121 updates each parameter according to formulas (12) to (14) by a normal steepest descent method.

  Here, μ is a step size for parameter update, and a relatively small value such as 0.0001 is used. Further, the co-occurrence relation model parameter calculation unit 121 repeats the calculations of Expressions (12) to (14) until the update amount of the parameter becomes sufficiently small by being controlled by the convergence determination unit 13 as described later. . Note that n ^ and n ~ in the equations (9) to (11) representing the gradient calculation of each parameter are calculated as follows each time the calculation is repeated.

[Calculation of n ^ _{t, i, j} ]
<Procedure a1>
First, p (n _{i, j} = 1 | x _j ) is calculated using conventional clustering-based sound source separation or the like.
<Procedure a2>
Next, the initial value of n ^ _{t, i, j} is sampled from p (n _{i, j} | x _{j, t} ). Here, a specific processing example of sampling will be described. First, the posterior probabilities that the observed feature quantity x ^ _{t, i at} time t and node i belong to cluster j are calculated for clusters 1 to J. At this time, the sum of the posterior probability values from 1 to J is 1. Next, based on these posterior probabilities, the 0 to 1 section is divided. For example, if the attribution posterior probabilities calculated for clusters 1, 2, and 3 are 0.1, 0.7, and 0.2, respectively, the interval from 0 to 1 is divided into [0.0 0.1), [0.1 0.8), and [0.8 1.0]. Associate each section with each cluster. Then, one random number in the range of 1 to 0 is generated, and it is detected to which section the random number belongs. As appropriate corresponding to the segment class n ^ _{t to, i,} and 1 _j, is n ^ _t in the same microphone node _{otherwise, i,} a _j and 0.
<Procedure a3>
The following (a3.1) and (a3.2) are repeated a predetermined number of times. (In this example, once)
(A3.1) Based on the currently obtained n ^ _{t, i, j} , a ^ _{t, k} is calculated using equation (6).
(A3.2) a ^ _{t, k} and x _{t, i} (observed feature quantity at microphone node i, time t) and p (x _i | n _{i, Based on j} = 1), n ^ _{t, i, j} is calculated using Equation (7) and Equation (8).
<Procedure a4>
Equations (9) to (11) are calculated using n ^ _{t, i, j} calculated in step a3.

[Calculation of n ~ _{t, i, j} ]
<Procedure b1>
The initial values of n ~ _{t, i, j} are sampled from p (n _{i, j} | x _{j, t} ). (Specific processing examples are the same as in step a2)
<Procedure b2>
The following (b3.1) and (b3.2) are repeated a predetermined number of times. (In this example, once)
(B3.1) Calculate a to _{t, k} using equation (6) based on the currently obtained n to _{t, i, j} .
(B3.2) Based on a to _{t, k} , n to _{t, i, j} is calculated using Equation (7).
<Procedure b3>
Equations (9) to (11) are calculated using n to _{t, i, j} calculated in step b3.

Then, the second sound source existence posterior probability calculation unit 122 of the sound source existence posterior probability co-occurrence pattern detection unit 12 between the microphone nodes updates the update information based on Expression (8) from the obtained parameters {W _i , b _i , c}. The second sound source existence posterior probability p (n _{i, j} = 1 | a ^ _t , x _t ) is calculated.

[Processing by sound source existence posterior probability estimation unit 11 by microphone node (processing after initial value calculation)]
The first sound source existence posterior probability update unit 112 of the sound source existence posterior probability estimation unit 11 by microphone node is a second sound source existence posterior probability p which is update information obtained by the microphone node sound source existence posterior probability co-occurrence pattern detection unit 12. Using (n _{i, j} = 1 | a ^ _t , x _t ), the distribution parameter of p (x _i | n _i ; θ ⁽ⁿ _i ⁾ ) is updated so that Equation (2) is maximized. Below, an example of the update method is shown.

First, the first sound source presence posterior probability update unit 112 represents p (x _i | n _i ; θ ⁽ⁿ _i ⁾ ) in Expression (2) as Expression (15).

Equation (15) represents p (x _i | n _i ; θ ⁽ⁿ _i ⁾ ) as a general exponential distribution family function. Here, the gradient related to θ ⁽ⁿ _i ⁾ of the logarithm of the likelihood formula of Formula (15) (log likelihood function) is expressed by the following Formula (16).

  At this time, the first sound source presence posterior probability update unit 112 approximately obtains p (n, a | x) as shown in the following equation (17).

The value of the equation (17) is the second sound source existence posterior probability p calculated based on the equation (8) obtained at the final stage of the processing in the microphone stage sound source existence posterior probability co-occurrence pattern detection unit 12 in the previous stage. This corresponds to an average value of (n _{i, j} = 1 | a ^ _t , x _t ) for all time frames t. Finally, the first sound source presence posterior probability updating unit 112 places the value of equation (17) as shown in the following equation (18) so that the value of equation (17) becomes 0, and solves the equation to obtain the value of θ ⁽ⁿ _i ⁾ . Calculate

If the value of θ ⁽ⁿ _i ⁾ in equation (18) is calculated, the first sound source existence posterior probability p (n _{i, j} | x _i ) can be calculated again, and this value is used as the sound source between microphone nodes. When output to the existence posterior probability co-occurrence pattern detection unit 12, the parameter {W _i , b _i , c} is updated again by the sound source existence posterior probability co-occurrence pattern detection unit 12.

[Processing at convergence determination unit 13]
The convergence determination unit 13 repeatedly performs the processes in the first sound source presence posterior probability update unit 112, the co-occurrence relation model parameter calculation unit 121, and the second sound source presence posterior probability calculation unit 122, and the sound source existence by microphone node of Expression (18) When the update amount of the parameter θ ⁽ⁿ _i ⁾ of the posterior probability estimation unit 11 and the parameter θ ^(w) of the sound source presence posterior probability co-occurrence pattern detection unit 12 of Equation (2) is equal to or less than a predetermined threshold, It determines that it has converged, and controls to end the repetition. Moreover, you may determine with having converged when the likelihood shown to Formula (2) became a sufficiently large value.

[Evaluation experiment]
An evaluation experiment was performed for the purpose of evaluating the performance of the sound source separation device according to the embodiment. The experimental conditions were as follows. FIG. 3 shows the acoustic environment used for the simulation. FIG. 3 is a diagram illustrating an acoustic environment in which the sound source separation device according to the embodiment is used. The size of the room was 10 m (W) × 5 m (D) × 5 m (H), and the reverberation time was four conditions of 0.2, 0.4, 0.6, and 0.8 seconds. This acoustic environment is mirror image method (Reference 2: JB Allen and DA Berkeley, “Image method for efficiently simulating small-room acoustics,” J. Acoust. Soc. Am., Vol. 65 (4), pp. 943-950, 1979. .).

  In order to simulate an environment with background noise, white noise was generated on a computer and added to the signal so that the S / N ratio was 10 dB to create an observation signal. There are 6 speakers, and 3 out of 6 speakers sit on the left side of the room in a circle with a radius of 80 cm, and the other 3 people are equally spaced in a circle with a radius of 80 cm. I assumed the situation where everyone was talking at the same time. This simulates a conversation situation in a conference room or a restaurant. As the sound collection device, as shown in FIG. 3, a situation is assumed in which there are two microphone nodes including three microphones.

The conventional method compared with the present invention is the method shown in Non-Patent Document 1 that performs sound source separation using a soft mask, assuming a common sound source posterior probability in all microphones.
SIR (Signal-to-interference ratio) indicating sound source separation performance was used as an evaluation index. The sound source separation performance indicates that the larger the SIR value, the better the performance. For evaluation speech, TIMIT (Reference 3: W. Fisher, GRDoddington, and KMGoudie-Marshall, “The DARPA speech recognition research database: specifications and status,” in Proc. DARPA workshop on Speech Recognition, 7986, pp. 96-99. .) Was randomly extracted from each sound environment, a total of 20 different mixed sounds were prepared in each acoustic environment, and the result was calculated as an average value thereof.

  FIG. 4 shows the results of the evaluation experiment. FIG. 4 is a diagram illustrating the sound source separation performance of the sound source separation device according to the embodiment. The horizontal axis represents the reverberation time, and the vertical axis represents the SIR value, that is, the sound source separation performance (dB). In all reverberant environments, the present invention has been shown to achieve higher performance than conventional methods. Thus, according to the sound source separation apparatus of the present invention, it was confirmed that sound source separation was performed efficiently even in a distributed microphone array environment.

[Processing in output sound estimation unit 14]
When the convergence determination unit 13 determines that the update amount has converged, the output sound estimation unit 14 performs filtering using the second sound source existence posterior probability, and estimates a sound source image related to each sound source.

[Processing flow of the embodiment]
A processing flow of the sound source separation device 10 according to the embodiment will be described with reference to FIG. FIG. 5 is a flowchart illustrating processing of the sound source separation device according to the embodiment. First, the sound source presence posterior probability estimation unit 11 for each microphone node calculates an initial value of the first sound source presence posterior probability (step S101). Next, the sound source presence posterior probability co-occurrence pattern detection unit 12 between the microphone nodes calculates a parameter for modeling the co-occurrence relationship of the first sound source presence posterior probability, and the already calculated existing parameter exists. Updates the existing parameters (step S102). Then, the sound source presence posterior probability co-occurrence pattern detection unit 12 between the microphone nodes calculates the second sound source presence posterior probability based on the calculated parameters (step S103).

  Here, when the convergence determination unit 13 determines that each update amount is not equal to or less than the threshold (No in step S104), the microphone node-specific sound source existence posterior probability estimation unit 11 performs the first determination based on the second sound source presence posterior probability. The posterior probability of one sound source is updated (step S105). Then, the inter-microphone sound source presence posterior probability co-occurrence pattern detection unit 12 performs the process again using the updated first sound source presence posterior probability.

  On the other hand, when the convergence determination unit 13 determines that each update amount is equal to or less than the threshold (Yes in step S104), the inter-microphone sound source existence posterior probability co-occurrence pattern detection unit 12 determines that the second sound source exists. The posterior probability is output to the output sound estimation unit 14 (step S106). Finally, the output sound estimation unit 14 performs sound source separation using the sound source existence posterior probability for each time as a filter (step S107).

[Effect of the embodiment]
First, the sound source separation device 10 calculates a sound source existence posterior probability for each sound source in each microphone node based on a microphone node observation signal that is a multi-channel observation signal obtained by collecting sound source signals emitted from a plurality of sound sources by a plurality of microphones. The sound source existence posterior probability is updated based on update information that is information for updating the sound source existence posterior probability. Then, the sound source separation device 10 assumes that the sound source existence posterior probabilities for each sound source co-occur in each microphone node in the same time frequency bin, models the co-occurrence relationship of the sound source existence posterior probabilities, and The parameters in the model are estimated so that the co-occurrence of the sound source posterior probability increases, and update information is calculated based on the parameters. Furthermore, the sound source separation device 10 performs the update of the sound source presence posterior probability in the sound source existence posterior probability estimation unit by sound microphone node and the calculation of update information in the sound source presence posterior probability co-occurrence pattern detection unit between microphone nodes, Repeat until the parameters converge. Finally, the sound source separation apparatus 10 estimates the sound source signal of each sound source by filtering the microphone node observation signal using the sound source existence posterior probability or the update information.

  As a result, it is possible to create a model of a sound source posterior probability in consideration of co-occurrence, and it is possible to use information obtained from a plurality of microphone nodes for sound source separation. As a result, sound source separation can be performed effectively even in a distributed microphone array environment.

[Equipment configuration]
When the processing means in the sound separation device 10 is realized by a computer, the processing contents of the functions that each device should have are described by a program. Then, by executing this program on the computer, the processing means in each apparatus is realized on the computer.

  Although the method using the contrastive divergence method has been described for the purpose of efficiently estimating RBM parameters, the present invention is not limited to this embodiment. Moreover, although the method of setting the value of Formula (16) to zero for the estimation of the distribution parameter in the sound source existence posterior probability estimation unit 11 by microphone node has been described, the present invention is not limited to this embodiment. For example, in order to maximize the value of the expression (2), the use of the all combination search method for searching all combinations of all parameters is included in the scope of the technical idea of the present invention.

  The program describing the processing contents can be recorded on a computer-readable recording medium. As the computer-readable recording medium, for example, any recording medium such as a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory may be used. Specifically, for example, as a magnetic recording device, a hard disk device, a flexible disk, a magnetic tape or the like, and as an optical disk, a DVD (Digital Versatile Disc), a DVD-RAM (Random Access Memory), a CD-ROM (Compact Disc Read Only). Memory), CD-R (Recordable) / RW (Re Writable), etc., MO (Magneto Optical Disc) etc. as the magneto-optical recording medium, EEP-ROM (Electronically Erasable and Programmable-Read Only Memory) etc. as the semiconductor memory be able to.

  The program is distributed by selling, transferring, or lending a portable recording medium such as a DVD or CD-ROM in which the program is recorded. Further, the program may be distributed by storing the program in a recording device of a server computer and transferring the program from the server computer to another computer via a network.

  Each means may be configured by executing a predetermined program on a computer, or at least a part of these processing contents may be realized by hardware.

  The sound source separation device 10 can physically and virtually disperse each function (device) included, and in this case, each function (device) in both devices may be dispersed as one unit. . For example, the convergence determination unit 13 may be omitted, and may be incorporated in the microphone node-specific sound source presence posterior probability estimation unit 11 or the inter-microphone node sound source presence posterior probability co-occurrence pattern detection unit 12. In addition, each unit in each device may be configured to be incorporated in another device to the extent that it functions effectively.

[program]
It is also possible to create a program in which processing executed by the sound source separation device 10 according to the above embodiment is described in a language that can be executed by a computer. In this case, the same effect as the above-described embodiment can be obtained by the computer executing the program. Further, such a program may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read by a computer and executed to execute the same processing as in the above embodiment. Hereinafter, an example of a computer that executes a sound source separation program that realizes the same function as the sound source separation device 10 will be described.

  FIG. 6 is a diagram illustrating a computer that executes a sound source separation program. As shown in FIG. 6, a computer 1000 includes, for example, a memory 1010, a CPU (Central Processing Unit) 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, a network Interface 1070. These units are connected by a bus 1080.

  The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM (Random Access Memory) 1012. The ROM 1011 stores a boot program such as BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1090. The disk drive interface 1040 is connected to the disk drive 1100. A removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100, for example. For example, a mouse 1110 and a keyboard 1120 are connected to the serial port interface 1050. For example, a display 1130 is connected to the video adapter 1060.

  Here, as shown in FIG. 6, the hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. Each table described in the above embodiment is stored in, for example, the hard disk drive 1090 or the memory 1010.

  The sound source separation program is stored in the hard disk drive 1090 as a program module in which a command executed by the computer 1000 is described, for example. Specifically, a program module describing each process executed by the sound source separation device 10 described in the above embodiment is stored in the hard disk drive 1090.

  Data used for information processing by the sound source separation program is stored as program data, for example, in the hard disk drive 1090. Then, the CPU 1020 reads out the program module 1093 and the program data 1094 stored in the hard disk drive 1090 to the RAM 1012 as necessary, and executes the above-described procedures.

  The program module 1093 and the program data 1094 related to the sound source separation program are not limited to being stored in the hard disk drive 1090. For example, the program module 1093 and the program data 1094 are stored in a removable storage medium and read by the CPU 1020 via the disk drive 1100 or the like. May be issued. Alternatively, the program module 1093 and the program data 1094 related to the sound source separation program are stored in another computer connected via a network such as a LAN (Local Area Network) or a WAN (Wide Area Network), and are transmitted via the network interface 1070. May be read by the CPU 1020.

DESCRIPTION OF SYMBOLS 10 Sound source separation apparatus 11 Sound source existence posterior probability estimation part according to microphone node 12 Sound source existence posterior probability co-occurrence pattern detection part 13 Convergence determination part 14 Output sound estimation part 15 Input part 20 Microphone node 21 Output apparatus

Claims (5)

Estimate the sound source existence posterior probability for each sound source at each microphone node based on the microphone node observation signal, which is a multi-channel observation signal obtained by collecting sound source signals emitted from a plurality of sound sources with a plurality of microphones, and A sound source existence posterior probability estimation unit for each microphone node that updates the sound source existence posterior probability based on update information that is information for updating the posterior probability;
Assuming that the sound source existence posterior probabilities for each sound source between the respective microphone nodes co-occur in the same time frequency bin, the co-occurrence relationship of the sound source existence posterior probabilities is modeled, and the sound source existence of each microphone node Estimating the parameters in the model so that the co-occurrence of the posterior probability is large, and calculating the update information based on the parameters, the sound source presence posterior probability co-occurrence pattern detection unit between microphone nodes,
The update of the sound source presence posterior probability in the sound source existence posterior probability estimation unit for each microphone node and the calculation of the update information in the sound source presence posterior probability co-occurrence pattern detection unit between the microphone nodes converge the sound source posterior probability or the parameter. A convergence determination unit that is repeatedly executed until
An output sound estimation unit that estimates the sound source signal of each sound source by filtering the sound source presence posterior probability or the update information with respect to the microphone node observation signal;
A sound source separation device comprising: The sound source existence posterior probability estimation unit for each microphone node is:
P (x _i | n _i ; θ ⁽ⁿ⁾ based on the observation feature quantity x _i obtained by collecting the sound source signals emitted from the plurality of sound sources in the time frame t as the microphone node observation signal at the i-th microphone node. _i ⁾⁾ ) to estimate the existence posterior probability p (n _{i, j} | x _i ) of the j th sound source at the i th microphone node, which is the sound source existence posterior probability, and based on the update information Re-estimating p (x _i | n _i ; θ ⁽ⁿ _i ⁾ ) so that the likelihood p (x; θ) of the observed signal is maximized. Update the existence posterior probability p (n _{i, j} | x _i ) at the i-th microphone node of
The sound source presence posterior probability co-occurrence pattern detection unit between the microphone nodes is
Sound source activity information at each microphone node using a set of p (n _{i, j} | x _{i, t} ) for all time frames t of the sound source existence posterior probabilities at all i, all j, and all t n and the potential probability of sound source activity common to all nodes a, and the sound source existence posterior so that the prior probability p (n, a; θ ^(w) ) indicating the sound source activity is maximized. The sound source separation apparatus according to claim 1, wherein θ ^(w) that is the parameter of the model indicating the co-occurrence of probability is estimated, and the update information is calculated. A sound source separation method executed by a sound source separation device,
Estimate the sound source existence posterior probability for each sound source at each microphone node based on the microphone node observation signal, which is a multi-channel observation signal obtained by collecting sound source signals emitted from a plurality of sound sources with a plurality of microphones, and Sound source presence posterior probability estimation step for each microphone node that updates the sound source presence posterior probability based on update information that is information for updating the posterior probability;
Assuming that the sound source existence posterior probabilities for each sound source between the respective microphone nodes co-occur in the same time frequency bin, the co-occurrence relationship of the sound source existence posterior probabilities is modeled, and the sound source existence of each microphone node Estimating the parameters in the model so that the co-occurrence of the posterior probability is large, and calculating the update information based on the parameters, the sound source presence posterior probability co-occurrence pattern detection step between microphone nodes;
The update of the sound source existence posterior probability in the sound source existence posterior probability estimation step for each microphone node and the calculation of the update information in the sound source presence posterior probability co-occurrence pattern detection step between the microphone nodes converge the sound source presence posterior probability or the parameter. A convergence determination step that is repeatedly executed until
An output sound estimation step of estimating the sound source signal of each sound source by filtering the microphone node observation signal using the sound source existence posterior probability or the update information;
A sound source separation method comprising: The microphone node-specific sound source existence posterior probability estimation step includes:
P (x _i | n _i ; θ ⁽ⁿ⁾ based on the observation feature quantity x _i obtained by collecting the sound source signals emitted from the plurality of sound sources in the time frame t as the microphone node observation signal at the i-th microphone node. _i ⁾⁾ ) to estimate the existence posterior probability p (n _{i, j} | x _i ) of the j th sound source at the i th microphone node, which is the sound source existence posterior probability, and based on the update information Re-estimating p (x _i | n _i ; θ ⁽ⁿ _i ⁾ ) so that the likelihood p (x; θ) of the observed signal is maximized. Update the existence posterior probability p (n _{i, j} | x _i ) at the i-th microphone node of
The microphone node sound source existence posterior probability co-occurrence pattern detection step includes:
Sound source activity information at each microphone node using a set of p (n _{i, j} | x _{i, t} ) for all time frames t of the sound source existence posterior probabilities at all i, all j, and all t n and the potential probability of sound source activity common to all nodes a, and the sound source existence posterior so that the prior probability p (n, a; θ ^(w) ) indicating the sound source activity is maximized. The sound source separation method according to claim 3, wherein θ ^(w) that is the parameter of the model indicating the co-occurrence of probability is estimated, and the update information is calculated.   A sound source separation program for causing a computer to function as the sound source separation device according to claim 1.

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