JP2010145836A - Direction information distribution estimating device, sound source number estimating device, sound source direction measuring device, sound source separating device, methods thereof, and programs thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To fit one probability distribution to each peak of sound information. <P>SOLUTION: When distribution of sound information from a sound source has a plurality of peaks, M probability distribution models are used and one probability distribution model is fit to each of the respective peak. Respective parameters of a current probability distribution model are held, and the sound information and the respective parameters of the current probability distribution model are used to calculate posterior probabilities by M probability distribution models. The sound information and the posterior probabilities by the M probability distribution models are used to update the respective parameters of the current probability distribution model. When it is determined that respective parameter values converge, the respective updated parameters are output. When, however, it is determined that respective parameter values do not converge, the respective updated parameters are held as respective parameters of the current distribution model in a parameter holding section and a Dirichlet distribution is used as a prior distribution of mixed weights among the respective parameters. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a direction information distribution estimation device, a sound source number estimation device, a sound source direction measurement device, a sound source separation device, a method thereof, and a program thereof used for acoustic signal processing.

  Conventionally, there is a technique for estimating the distribution of direction information of a sound source. For example, in the field of acoustic signal processing, this technique is used when estimating the direction of each person or separating and extracting each person's voice when a signal in which voices spoken by a plurality of people are simultaneously observed. Is important to.

  FIG. 1 shows an example of a functional configuration of a conventional direction information distribution estimation apparatus 100, and FIG. 2A shows an example of a histogram H obtained for sound direction information. The direction information is input to the direction information distribution estimation apparatus 100. Is. The principle of the direction information distribution estimation apparatus 100 is described in Non-Patent Document 1. The horizontal axis of FIG. 2A, FIG. 2B mentioned later, and FIG. 2C shows the angle of the sound source (sound arrival direction). The purpose of the direction information distribution estimation apparatus 100 is to fit (approximate) a normal distribution model to the histogram H. In particular, this technique considers a case where each of a plurality of distribution peaks in the histogram H is meaningful. For example, there is a case where each distribution peak represents estimated direction information such as an audio signal source or a radio signal source. In the example of FIG. 2A, there are four distribution peaks, and the four distribution peaks are denoted by a to d, respectively. The distribution peak a is approximately −115 degrees, the distribution peak b is approximately −20 degrees, the distribution peak c is approximately 60 degrees, and the distribution peak d is approximately 150 degrees.

  When estimating the sound source direction from the histogram H, an average value of the angle of the distribution peaks is obtained. In order to obtain this average value, each distribution mountain is fitted with one probability distribution model. Is required.

  In the conventional technique, the entire histogram is modeled by a mixed normal distribution model (GMM), for example. The mixed normal distribution G is represented by the following formula (1).

The processing of each component of the conventional direction information distribution estimation apparatus 100 will be briefly described with reference to FIG. First, direction information (histogram H shown in FIG. 2A) is input to the posterior probability calculation unit 2, and the posterior probability calculation unit 2 obtains the posterior probability for each of the M normal distribution models. Then, the M posterior probabilities are input to the parameter update unit 4. The average updating unit 42, the distributed updating unit 44, and the mixture weight updating unit 46 update the μ _m , σ _m , and α _m using the EM algorithm while holding the parameter holding unit 8 using M posterior probabilities and the like. To do. Details of the update process are omitted.

For example, when the number of updates exceeds the threshold T, the post-convergence parameter θ _v (θ _v = (μ _m , σ _m , α _m ) m = 1,..., M) is output. Using the outputted after convergence parameter theta _v, the separation of the sound source direction of the measurement or sound.
M.Mandel, D.Ellis, and T.Jebara, "An EM algorithm for localizing multiple sound sources in reverberant environments," Proc.Neural Info.Proc.Sys., 2006.

FIG. 2B shows the result of fitting by the direction information distribution estimation apparatus 100 using eight normal distribution models in the histogram H shown in FIG. 2A. Note the location around −115 degrees in FIG. 2B (the location P in FIG. 2B). At the point P, two normal distribution models are fitted even though the distribution near −115 degrees is desired to be fitted with one normal distribution model. Then, the normal distribution models shown in FIG. 2B are summed to obtain the mixed normal distribution model shown in FIG. 2C, and the post-convergence parameter θ _{v of the} obtained mixed normal distribution model is output from the direction information distribution estimation device 100. Is done. With this case, there is a problem that since the two normal distributions model P has gone fitting, it is impossible to obtain an accurate after convergence parameter theta _v. As a result, accurate sound source separation and sound source direction estimation cannot be performed.

  In the present invention, when direction information having a plurality of peaks is given as sound information from a sound source, a direction information distribution estimation device capable of fitting one probability distribution model to each peak is provided. is there.

  In the present invention, when sound information from a sound source has a plurality of peaks, a direction information distribution is used for fitting one probability distribution model to each peak using M (M is an integer of 1 or more) probability distribution models. It is an estimation device. The direction information distribution estimation device includes a parameter holding unit, a posterior probability calculation unit, and an update unit. The parameter holding unit holds each parameter of the current probability distribution model. The posterior probability calculation unit calculates the posterior probability for each of the M probability distribution models using the sound information and each parameter of the current probability distribution model. The update unit updates each parameter of the current probability distribution model using sound information and the posterior probability for each of the M probability distribution models, and is updated when it is determined that each parameter value has converged. When each parameter is output and it is determined that each parameter value has not converged, each updated parameter is stored in the parameter storage unit as each parameter of the current probability distribution model. And among each parameter, Dirichlet distribution is used for the prior distribution of mixing weight.

  In the direction information distribution estimation device of the present invention, by giving a Dirichlet distribution as a prior distribution to the mixture weights that are parameters of the probability distribution model, a small number of probability distribution models can be fitted to each peak, and as a result, One probability distribution model can be fitted to each peak.

  The best mode for carrying out the invention will be described below. In addition, the same number is attached | subjected to the process which performs the structure part which has the same function, and the same process, and duplication description is abbreviate | omitted.

  FIG. 3 shows a functional configuration example of the direction information distribution estimation apparatus 200 according to the first embodiment, and FIG. 4 shows a processing flow. FIG. 5A shows the input histogram H, and FIGS. 5B and 5C show examples of distributions obtained by the direction information distribution estimation apparatus 200, respectively. In addition, the histogram H in FIG. 5A is the same as that in FIG. 2A.

In the first embodiment, an example in which M normal distributions are used as the M probability distribution models to be used, the input sound information is the direction information D, and the direction information D is an example of the histogram H is shown. When the sound information is direction information, the horizontal axis represents the angle and the vertical axis represents the frequency. Then, when the histogram H has a plurality of peaks, the direction information distribution estimation apparatus 200 fits one probability distribution model to each peak. Any model other than the normal distribution model may be used as long as it is a probability distribution model. Here, each peak corresponds to one sound source direction.
Normally, the direction information D includes an indefiniteness of 2π times k (k is an integer), and therefore a Wrapped GMM that allows it is used here. G, which is a Wrapped GMM, can be expressed by the following equation (2).

Here, θ represents the average of the mixed normal distribution μ, variance σ, and mixing weight α, that is, θ = (μ _m , σ _m , α _m ) = (μ ₁ , σ ₁ , α ₁ ,. _{, μ m, σ m, α} m, ..., μ M, σ M, the α _M). Also, assuming that t is the number of updates (time) and θ has the concept of the number of updates, that is, θ updated t times is θ ^t , θ ^t = (μ ₁ ^t , σ ₁ ^t , α ₁ ^{t _{, ..., μ m t, σ}} m t, α m t, ..., μ M t, σ M t, the α _M ^t). The storage unit 16 stores the number M of normal distribution models used in advance and the initial value θ ⁰ of each parameter of the mixed normal distribution model. The prior distribution information holding unit 110 holds a hyper parameter φ and a weight parameter c described later.

The direction information distribution estimation apparatus 200 includes N pieces of direction information D = {d ₁ ,. . . , D _n ,. . . , D _N } and weighting factors A = {a ₁ ,. . . , _A n,. . . , A _N }. The weight coefficient A is a weight coefficient for each element d _n (n = 1,..., N) of the direction information. This weighting coefficient can be given by, for example, the frequency with which the direction information D is obtained or the reliability when the direction information D is obtained (power of the acquired signal, instantaneous signal-to-noise ratio of the signal, etc.). Alternatively, a _n = 1 may be set for all n.

First, t = 0 is set (that is, the number of updates is 0), the value of the parameter θ ⁰ of the mixed normal distribution when t = 0 is set, and the normal distribution model number M to be used, K, which is the range of k, An update count threshold T or a difference threshold Δ (described later) is set. The update count threshold T or the difference threshold Δ is used in the convergence determination process described later (step S2).

The posterior probability calculation unit 12 calculates sound information (direction information D in the first embodiment) and the current mixed normal distribution parameter θ ^t (= (μ _m ^t , σ _m ^t , α _m ^t m = 1,. , M)), the posterior probability p (m, k | d _n , θ ^t ) is calculated for each of M normal distributions (step S6). The parameter holding unit 18 holds θ ^t of the current mixed normal distribution. Specifically, the posterior probability calculation unit 12 calculates, for example, by the following equation (5).

The molecule “p (m, k, d _n | θ ^t ) on the right side of the equation (5) is θ in“ p (m, k, d _n | θ) ”represented by the above equations (3) and (4). Is given the concept of the number of updates t.

Next, the updating unit 14 updates each parameter θ ^t of the current mixed normal distribution by using the direction information D and the posterior probability p (m, k | _dn , θ ^t ) (step S8). Hereinafter, the update process will be described in detail. The update unit 14 extracts the hyper parameter φ and the weight parameter c from the prior distribution information holding unit 110 during the update process. In the present invention, the update process of the parameter θ is performed by giving an appropriate prior distribution to the mixture weight α _m of the parameter θ of the normal distribution, for example, by the EM algorithm. In the first embodiment, a Dirichlet distribution is considered as a prior distribution of the mixture weight α _m . The details of the Dirichlet distribution can be found in Reference 1, “CM Bishop (translated by Motoda, Kurita et al.)“ Pattern Recognition and Machine Learning (above) ”, Springer Japan 2007, p. 74-p. 77 "and the like. The Dirichlet distribution is expressed by, for example, the following formula (6).

Where α is the mixing weight matrix, α = {α ₁ ,. . . , Α _m ,. . . , Α _M }, and satisfies the condition that Σ _m ^M α _m = 1 and 0 ≦ α _m ≦ 1. Note that this is the same as the condition of the mixture weight, which is a parameter of the mixture normal distribution. Β (φ) is a normalization term (beta distribution). Here, when the hyperparameter φ is set to a positive value smaller than 1 (eg, 0.9), only a very small number of α _m is sufficiently large. It has a value, and the rest takes a value close to 0. By applying this property to the mixture weight α _m of the mixed normal distribution G expressed by the expression (1), only a small number of normal distributions in the mixed normal distribution G are sufficiently large. The mixture weight of the normal distribution is close to zero. As a result, fitting with as few normal distributions as possible is possible.

  Next, an EM algorithm for performing parameter update is derived while including this prior distribution. First, the cost function L (θ) for maximum likelihood estimation is given as follows.

The weight parameter c is a parameter for controlling the weights of the first term and the second term in the equation (9).

It becomes. Here, E [H] in Expression (11) indicates the expected value of Expression H, and p (m, k | _dn , θ ^t ) in Expression (12) is the posterior probability expressed by Expression (5). Distribution. Note that there is no log (p (α)) in equation (12) in the conventional EM algorithm.

Also as described above, in this case, sound information N pieces of direction information indicating the direction of arrival of sound _{d n (n = 1, ...} , N) is, 2Keipai times in the direction information _{d n} ( k is an integer), the mixed normal distribution is a wrap GMM, c is a weight parameter, φ is a hyperparameter, and K indicates a range of k.

The average updating unit 142 in the updating unit 14 in FIG. 3 updates the current average μ _m ^t from the equation (13), thereby outputting the updated average μ _m ^{t + 1} . The variance updating unit 144 updates the variance σ _m from the equation (14), and outputs the updated variance σ _m ^{t + 1} . The mixture weight updating unit 146 updates the mixture weight α _{m according} to the equation (15), thereby outputting the updated mixture weight α _m ^{t + 1} . The parameter calculation means calculates the updated parameter θ ^{t + 1} for the updated average μ _m ^{t + 1} , variance σ _m ^{t + 1} , and mixture weight α _m ^{t + 1} .

The update process of each parameter is performed several times (step S4), and the convergence determination unit 150 in the update unit 14 determines whether each parameter value has converged with respect to the updated θ ^{t + 1 according} to a predetermined rule. Whether or not convergence is judged is performed (step S10). If it is determined that each parameter value has converged, the updated parameter θ ^{t + 1} is output. When it is determined that the parameter values have not converged, the updated parameter θ ^{t + 1} is held in the parameter holding unit 18 as the average, variance, and mixture weight of the current probability distribution model. And the process of step S4-step S10 is repeated until the convergence determination means 150 judges that each parameter value has converged.

Here, an example of a predetermined rule used for convergence determination will be described. Explaining an example of using the update count threshold T, the counting means (not shown) in the update unit 14 counts the update count t, and if the update count t exceeds the update count threshold T, the update means 14 updates sufficiently. Therefore, the updated parameter θ is output. Further, an example using the difference threshold Δ will be described. When the following equation (16) is satisfied, it is determined that it has converged, and the updated parameter θ is output.
| Q (θ | θ ^{t + 1} ) −Q (θ | θ ^t ) | <Δ (16)
Θ calculated by the parameter calculation means 148 is each parameter of the mixed normal distribution of FIG. 5C.

Further, in the first embodiment, if K = 0, it is possible to perform fitting using a normal GMM instead of a wrap GMM. In this case, the sound information D need not be direction information. For example, when sound from a sound source is collected by J microphones 20 _j (j = 1,..., J), the microphones 20 _j and 20 _{j ′} (j ′ = 1,. , J, j ≠ j ′) and the microphone phase difference q ′ _{jj ′} may be used as the sound information D. Further, in the first embodiment, the prior distribution is introduced only to the mixture weight α _m, but by introducing the prior distribution to the average μ _m and variance σ _{m of} each Gaussian distribution, more accurate GMM fitting (direction) Information distribution estimation processing) can be realized. In addition to the EM algorithm, various algorithms such as Bayesian estimation can be used for GMM fitting when the prior distribution is introduced into each parameter of each Gaussian distribution, mean μ _m , variance σ _m , and mixture weight α _m . It has been known. These extensions can be easily realized by those skilled in the art with reference to the above-described reference document 1 and the like, and are omitted here.

As described in the first embodiment, when the hyperparameter φ in the equation (5) is set to a positive value smaller than 1 (for example, 0.9), only a very small number of α _m is sufficiently obtained due to the nature of the Dirichlet distribution. It has a large value, and the rest takes a value close to 0. Only a small number of normal distributions of the GMM shown in the above formula (1) have a sufficiently large mixture weight α _m , and the weights of other normal distributions are close to zero. By using this property, model fitting with as few normal distributions as possible is possible.

  In the first embodiment, M normal distribution models are used as the M probability distribution models. In the second embodiment, M von Mises distribution models are used. The von Mises distribution is a function representing the angular distribution, and details of the von Mises distribution model are given in Reference 2 “KV Mardia,“ Statistics of Directional Data ”, Academic Press, 1972, 3.4.9. Section "etc. The effect of using the von Mises distribution is that it is not necessary to consider the values of k and K, as compared with the case of using the normal distribution model, and the arithmetic processing is reduced.

The functional configuration example and the processing flow of the direction information distribution estimation apparatus 300 according to the second embodiment are almost the same as those in FIGS. 3 and 4, but the distributed update unit 144 in FIG. 3 is replaced with the diffusion parameter update unit 160. The point is different. Details will be described below. Further, the parameter θ of the von Mises distribution model is θ = {μ _m , к _m , α _m }, and к _m is a diffusion parameter.

First, the posterior probability calculation unit 12 has M pieces of sound information D (for example, direction information D) and M from the current parameters θ ^t = {α _m ^t , μ _m ^t , к _m ^t } held in the parameter holding unit 18. A posteriori probability p (m | d _n , θ ^t ) for the von Mises distribution model is obtained.

The equation (17) are those which correspond to the formula (4), molecules ^{_{p (m, d n │θ t}} ) on the right side in the equation (17), Von Mises distribution g _{(d n;} μ _m, is a к _m).

Here, −π <d _n ≦ π, −π <μ _m ≦ π, and к _m > 0. I ₀ (x) is a 0th-order modified Bessel function of the first type.

Next, the update unit 14 uses the sound information D and the posterior probability p (m | d _n , θ ^t ), and the parameter θ of the von Mises distribution, that is, the average μ _m ^t , the diffusion parameter к _m ^t , and the mixing Update the weight α _m ^t . Details will be described below.

The average updating unit 142 updates the average μ _m ^t by, for example, the following equation (20).

Here, arctan (x) generally returns a value of −π / 2 <μ _m <π / 2. Therefore, in order to handle data of −π <μ _m <π, the following calculation is also performed. .
When the value of Expression (20) is negative, the second derivative of the Q function shown in Expression (21) is calculated for both μ _m ^t and μ _m ^t + π, and the value of Expression (21) becomes negative. Is μ _m ^{t + 1} .

When the value of the expression (20) is positive, the expression (21) is calculated with respect to μ _m ^t and μ _m ^t −π, and the one that becomes negative is stored as μ _m ^t .
The diffusion parameter update unit 160 updates, for example, by the following equation (22).

Here, I (к _m ^{t + 1} ) is a diffusion parameter function. к _m ^{t + 1} cannot be obtained analytically, but can be obtained as follows. The diffusion parameter function I (к _m ^{t + 1} ) is a monotonically increasing function. Therefore, a lookup table in which “к _m ^{t + 1} ” and “I (к _m ^{t + 1} )” are prepared for a certain range of к (for example, 0 ≦ к ≦ 100) is prepared. The lookup table may be stored in a storage unit (not shown) in the diffusion parameter update unit 160. When the I (к _m ^{t + 1)} is obtained, by referring to the lookup table, and outputs the к _m ^{t + 1} which corresponds to ^{_{I (к m t + 1)}} .
For example, the mixing weight updating unit 146 updates the mixing weight α _m by the following equation (23).

In this way, the update unit 14 updates the distribution parameter θ ^t (= {α _m , μ _m , к _m }).
Since the von Mises distribution is used as in the direction information distribution estimation apparatus 300 of the second embodiment, an estimation operation regarding k is unnecessary, so that the calculation is performed in comparison with the direction information distribution estimation apparatus 200 of the first embodiment. The convergence time of the cost and parameter θ can be reduced.

[Experimental result 1]
The result of the fitting experiment by the direction information distribution estimation apparatus 200 described in the first embodiment will be described with reference to FIG. As an experimental condition, a mixed normal distribution composed of 8 (= M) normal distributions is fitted, and the hyperparameter φ is set to 0.9. Showed similar histograms H and Figure 2A in the direction information d _n inputted in Figure 5A, as described above, it shows the results of a normal distribution by fitting process direction information distribution estimation apparatus 200 in FIG. 5B, FIG. In FIG. 5C A mixed normal distribution (GMM) obtained by summing up the normal distribution of 5B is shown. If attention is paid to the point P (around −115 degrees) shown in FIG. 5B, it will be understood that fitting can be performed with one normal distribution. Therefore, the obtained mixed normal distribution shown in FIG. 5C is accurate. Therefore, the sound source number estimation processing, sound source direction measurement processing, and sound source separation processing described in Embodiments 3 to 5 can be performed accurately.

  On the other hand, as described above, as for the experimental result of the conventional direction information distribution estimation apparatus 100 shown in FIG. 1B, two normal distributions are fitted at the position P, and the GMM shown in FIG. It becomes a thing.

In the third embodiment, a sound source number measuring apparatus 400 using the direction information distribution estimating apparatuses 200 and 300 described in the first and second embodiments will be described. FIG. 6 shows a functional configuration example of the sound source number measuring apparatus 400. The sound source number measuring apparatus 400 according to the third embodiment is described as being connected to J (J is an integer of 2 or more) sound collecting means 20 _j (for example, microphone j = 1,..., J). To do. When sound is emitted from a plurality of sound sources within a certain recording time (for example, 5 seconds), it is assumed that the sound is recorded by J sound collecting means 20 _j (hereinafter referred to as situation X). The sound source number measuring apparatus 400 according to the third embodiment estimates the number of sound sources that emit sound using only the recorded sound.

The sound signal input from the sound collection means 20 _j is x _i (s), and s is a discrete time. The frequency domain converter 30 converts the sound signal x _i (s) into a frequency domain sound signal X _j (f, τ). f is a frequency and τ is a time frame number. In the third embodiment, n = τF + f is considered. Where F is the number of frequency regions.
The power estimation unit 32 obtains sound power from the frequency domain sound signal. As an example of how to obtain, the power estimation unit 32 calculates and outputs the signal power | X _j (f, τ) | ² of the frequency domain sound signal X _j (f, τ) at each time frequency (f, τ). . The signal output power _{│X j (f, τ) │} 2 is, as the weighting factor _{a n} described above, is used thereafter.

Moreover, the arrival direction estimation part 34 calculates | requires the arrival direction information of a sound from a frequency domain sound signal. An example of how to find out will be described in detail. The arrival direction estimating unit 34 includes a phase difference calculating unit 342 and a direction of arrival information generating unit 344. First, the phase difference calculating means 342 between the sound collecting means calculates the phase difference q ′ _{jj ′} (f, τ) between the sound collecting means for all combinations (microphone pairs) of the sound collecting means at each frame τ and each frequency f. It calculates | requires by the following formula | equation (24). However, j = 1,. . . , J, j ′ = 1,. . . , J, and j ≠ j ′.
q ′ _{jj ′} (f, τ) = {arg [X _j (f, τ) X ^* _{j ′} (f, τ)]} / 2πf
(24)

However, “ ^* ” indicates a complex conjugate. A vector in which all q ′ _{jj ′} (f, τ) are arranged is defined as Q ′ (f, τ). The sound arrival direction information Q (f, τ) is obtained by the following equation (25) using the sound velocity C and the coordinate system D of each sound collecting means.
Q (f, τ) = CD ⁺ Q ′ (f, τ) (25)

Here, C is the speed of sound, “ ⁺ ” represents a Moore-Penrose pseudo-inverse matrix, and D = [D ₁ -D _L ,. . . , D _j -D _L ,. . . , D _J −D _L ] ^T , D _j is a vector aligned with the coordinates (x, y, z) of the sound collection means 20 _j , and L is selected as a representative of the J sound collection means This is an index of representative sound collecting means. The xyz coordinates (x _Q , y _Q , z _Q ) of the arrival direction information Q (f, τ) have an arrival direction horizontal angle (hereinafter simply referred to as “horizontal angle”) as Ψ (f, τ), and an arrival direction. When the elevation angle (hereinafter simply referred to as “elevation angle”) is Ω (f, τ), it can be expressed by the following equation (26).
Q (f, τ) = (x _Q , y _Q , z _Q )
= (CosΨ (f, τ) cosΩ (f, τ),
sinΨ (f, τ) cosΩ (f, τ),
sinΩ (f, τ)) (26)

In this embodiment, only the horizontal angle Ψ (f, τ) is used. The obtained arrival direction information Q (f, τ) is used as the direction information d _n. Also, creating a histogram for the direction information d _n, the histogram H shown in FIG. 2A is obtained. Then, the direction information d _n, the direction information a _n are input to the direction information distribution estimation apparatus 200 (or 300), the processing described in Example 1 (or Example 2), the parameters θ are output. Hereinafter, the output parameter θ is set as the determined parameter θ.

The sound source number measurement unit 36 has a mixture weight larger than a predetermined first threshold value ε1 (for example, 10 ⁻⁶ ) among the mixture weights α _m (m = 1,..., M) of the parameter θ after determination. The number M ′ of direction information distribution models as values is measured. The measured number M ′ is output as the number of sound sources. This is because if the calculation of the direction information distribution estimation apparatus 200 (300) has sufficiently converged, the number having a sufficiently large value among the mixture weights α _m in the post-determined parameter θ is the peak of the distribution in the histogram. Because it becomes equal to the number of. In the following description, a direction information distribution model for a sound source recognized as a sound source is referred to as a sound source corresponding direction information distribution model.

Moreover, when the calculation of the direction information distribution estimation apparatus 200 (300) is not sufficiently converged, the sound source number measurement unit 36 preferably performs the following estimation process. First, when the direction information distribution estimation device 200 (described in the first embodiment) is used in the sound source number measuring device 400, the sound source number measuring unit 36 has a mixing weight α _m larger than the first threshold value ε1. A direction information distribution model whose variance σ _m is smaller than a predetermined second threshold value ε2 (for example, 15 degrees) is detected as a sound source corresponding direction information distribution model, and the number M ′ of the detected sound source corresponding direction information distribution models is detected. Can be measured. When the direction information distribution estimation device 300 (described in the second embodiment) is used in the sound source number measuring device 400, the sound source number measuring unit 36 has a mixing weight α _m larger than the first threshold value ε1. In addition, a direction information distribution model whose diffusion parameter к _m is larger than a third threshold (for example, 10) is detected as a sound source corresponding direction information distribution model, and the number M ′ of the detected sound source corresponding direction information distribution models may be measured. .

  When the conventional direction information distribution estimation apparatus 100 fits a normal distribution to each peak of a histogram, as shown in FIG. 2B, there are cases where two normal distributions are fitted to one peak. Therefore, if the direction information distribution estimation apparatus 100 is applied to the sound source number measuring apparatus, the wrong number of sound sources is measured. However, the direction information distribution estimation apparatus 200 (or 300) described in the first and second embodiments uses one probability distribution model (for example, a normal distribution model or von Mises) for one peak as shown in FIG. 5B. Distribution) can be fitted, so that the accurate number of sound sources can be measured.

  In the fourth embodiment, a sound source direction measuring apparatus 500 will be described. In the situation X, the sound source direction measuring apparatus 500 estimates the direction of the sound source using only the recorded sound. FIG. 7 shows a functional configuration example of the sound source direction measuring apparatus 500. In the example of FIG. 7, the sound source direction measuring device 500 includes a sound source number measuring device 400 (described in the third embodiment) and a sound source direction measuring unit 38.

When the processing of the sound source number measuring apparatus 400 is finished, the sound source direction measuring unit 38 determines the index m ′ {m ′ = 1,. . . Retrieves the 'average parameter mu _m corresponding _to}' M from direction information distribution estimation apparatus 200, and outputs the average parameter mu _{m 'as} the sound source direction to be estimated.

  As in the fourth embodiment, since the accurate direction information distribution process is performed by the direction information distribution estimation device 200 (300) included in the sound source direction measuring device 500, the sound source direction measuring device 500 has an accurate sound source direction. Measurements can be made.

  In the fifth embodiment, a sound source separation device 600 will be described. In the situation X, the sound source separation device 600 separates and extracts a sound signal from the sound source using only the recorded sound. FIG. 8 shows a functional configuration example of the sound source separation device 600. In FIG. 8, the sound source separation device 600 includes a sound source number measurement device 400 (described in the third embodiment), a separation unit 40, and a time domain conversion unit 41.

  When the processing of the sound source number measuring device 400 is completed, the separation unit 40 performs the following processing on the index m ′ of the sound source applicable direction information distribution model determined by the sound source number measuring device 400.

When the sound source number measuring apparatus 400 includes the direction information distribution estimating apparatus 200, the separation unit 40 performs the posterior about M ′ normal distributions (see Expression (5)) by the following Expression (27). probability p by marginalizing _{(m ', k│d n, θ} t) and determining the marginalized posterior probability ^{_{p (m'│d n, θ t)}} .
p (m ′ | d _n , θ) = Σ _{k = −K} ^K p (m ′, _k | d _n , θ ^t ) (27)
Further, when the sound source number measuring apparatus 400 includes the direction information distribution estimating apparatus 300, the calculation result of the above equation (17) is used without performing the marginalization process.

The frequency domain sound signal X _j (f, τ) from the frequency domain conversion unit 30 is input to the separation unit 40. The separation unit 40 multiplies the marginalized posterior probability and the frequency domain sound signal. That is, by calculating the following equation (28), a frequency domain target signal (separated signal) corresponding to the estimation of the m′-th signal is output.
Y _{nm ′} = X _n p (m _′ | _dn , θ) (28)
Here, X _n is obtained by transforming X ₁ (f, τ) by the above-described n = τF + f. It should be noted that the output frequency domain target signal is expressed by the following equation (29) from n = τF + f when the time frequency expression (f, τ) is used.
Y _{m ′} (f, τ) = X ₁ (f, τ) p ( _{m ′} | Ψ (f, τ), θ) (29)

Then, the time domain conversion unit 41 obtains and outputs the target signal y _{m ′} (t) by converting the frequency domain target signal Y _{m ′} (f, τ) to the time domain.
As in the fifth embodiment, since the accurate direction information distribution processing is performed by the direction information distribution estimation device 200 (300) included in the sound source separation device 600, the sound source separation device 600 performs accurate signal separation. be able to.

[Experimental result 2]
Next, a sound source number measuring device 400 (described in the third embodiment), a sound source separating device 600 (described in the fifth embodiment) using the direction information distribution estimating device 200 (hereinafter referred to as “invention method”), and the related art. Experimental results comparing the number-of-sound-sources measurement apparatus and the sound source separation apparatus (hereinafter referred to as “conventional method”) using the direction information distribution estimation apparatus 100 will be described. First, experimental conditions will be described with reference to FIG. Three microphones Z ₁ , Z ₂ , and Z ₃ are arranged at the vertices of an equilateral triangle in a room having a longitudinal direction of 4.45 m (= Lb), a lateral direction of 3.55 m (= La), and a height of 2.5 m. Is done. The interval between adjacent microphones is 4 cm, and the sound collecting surfaces of the three microphones are directed outward. The center of gravity of the equilateral triangle formed by the _three microphones Z ₁ , Z ₂ , and Z ₃ is 2.56 m (= Ld) in the longitudinal direction from the lower left vertex X in FIG. 9, and 1.8 m (= Lc) in the lateral direction. ). Also, two to four speakers (four speakers S ₁ , S ₂ , S ₃ , and S _{4 in} the example of FIG. 9) so as to surround the _three microphones Z ₁ , Z ₂ , and Z ₃ have a circumference R. It is arranged in the direction of The radius of the circumference R is 50 cm or 110 cm, and the sound reverberation time is 128 ms. The heights of the microphones Z ₁ , Z ₂ , Z ₃ and the speakers S ₁ , S ₂ , S ₃ , S ₄ are all 1.2 m.

  As for the experimental items, (1) Whether the number of sound sources can be measured when the number of sound sources (speakers) is 2, 3, or 4, (sound source number measurement processing), or (2) whether the sound signals from the sound sources can be separated. (Sound source separation processing). With respect to these items, experiments were conducted on 20 combinations by changing the sound quality of the sound emitted from the speaker or changing the radius of the circumference R formed by the speaker.

  FIG. 10 shows the experimental results under such conditions. In FIG. 10, with respect to the number of sound sources, the probability of determining the number W of sound sources out of 20 is evaluated, and the signal-to-interference ratio (SIR) is determined for the sound source separation processing. Evaluated. As understood from FIG. 10, it is understood that the conventional method outputs incorrect results for the sound source number processing and the sound source separation processing, but the invention method provides almost accurate results.

<Hardware configuration>
The present invention is not limited to the above-described embodiment. In addition, the various processes described above are not only executed in time series according to the description, but may be executed in parallel or individually according to the processing capability of the apparatus that executes the processes or as necessary. Needless to say, other modifications are possible without departing from the spirit of the present invention.
When the above configuration is realized by a computer, the processing contents of functions that the direction information distribution estimation device 200 (300), the sound source number estimation device 400, the sound source direction measurement device 500, and the sound source separation device 600 should have are described by a program. Is done. The processing function is realized on the computer by executing the program on the computer.

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

  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. Furthermore, the program may be distributed by storing the program in a storage device of the server computer and transferring the program from the server computer to another computer via a network.

A computer that executes such a program first stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own storage device. When executing the process, the computer reads the program stored in its own recording medium and executes the process according to the read program. As another execution form of the program, the computer may directly read the program from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to the computer. Each time, the processing according to the received program may be executed sequentially. Also, the program is not transferred from the server computer to the computer, and the above-described processing is executed by a so-called ASP (Application Service Provider) type service that realizes the processing function only by the execution instruction and result acquisition. It is good. Note that the program in this embodiment includes information that is used for processing by an electronic computer and that conforms to the program (data that is not a direct command to the computer but has a property that defines the processing of the computer).
In this embodiment, the present apparatus is configured by executing a predetermined program on a computer. However, at least a part of these processing contents may be realized by hardware.

  In addition, the direction information distribution estimation device 200 (300), the sound source number estimation device 400, the sound source direction measurement device 500, and the sound source separation device 600 described in this embodiment are a CPU (Central Processing Unit), an input unit, an output unit, and an auxiliary device. It has a storage device, a RAM (Random Access Memory), a ROM (Read Only Memory), and a bus (all not shown).

  The CPU executes various arithmetic processes according to the read various programs. The auxiliary storage device is, for example, a hard disk, an MO (Magneto-Optical disc), a semiconductor memory, or the like, and the RAM is an SRAM (Static Random Access Memory), a DRAM (Dynamic Random Access Memory), or the like. The bus connects the CPU, the input unit, the output unit, the auxiliary storage device, the RAM, and the ROM so that they can communicate with each other.

<Cooperation between hardware and software>
A direction information distribution estimation device, a sound source number estimation device, a sound source direction measurement device, and a sound source separation device according to the present invention allow a recording unit of a computer to read a program that operates as each component of the present invention, and a processing unit, an input unit This can be realized by operating the output unit. In addition, as a method of causing the computer to read, the program is recorded on a computer-readable recording medium, and the program recorded on the server or the like is read into the computer through a telecommunication line or the like. There is a method to make it.

The block diagram which showed the function structural example of the conventional direction information distribution estimation apparatus. A is a histogram input to a conventional direction information distribution estimation apparatus, B is a result of fitting a normal distribution by the conventional direction information distribution estimation apparatus, and C indicates a mixed normal distribution for these normal distributions. The figure which showed the direction information distribution estimation apparatus of a present Example. The figure which showed the processing flow of the direction information distribution estimation apparatus of a present Example. A is a histogram input to the direction information distribution estimation apparatus of the present embodiment, B is a result of fitting a normal distribution by the direction information distribution estimation apparatus of the present embodiment, and C is a mixed normal for these normal distributions. Show the distribution. The block diagram which showed the function structural example of the sound source number measuring apparatus of a present Example. The block diagram which showed the function structural example of the sound source direction measuring apparatus of a present Example. The block diagram which showed the function structural example of the sound source separation apparatus of a present Example. The figure which showed the experimental condition of the experiment 2. FIG. The figure which shows the result of the experiment 2. FIG.

Claims (13)

When the distribution of sound information from the sound source has a plurality of peaks, each parameter of the probability distribution is updated by using M probability distribution models (M is an integer of 1 or more), and each peak is updated. A direction information distribution estimation device for fitting one probability distribution model,
A parameter holding unit holding each parameter of the current probability distribution model;
A posterior probability calculator for calculating posterior probabilities for each of the M probability distribution models using the sound information and the parameters of the current probability distribution model;
Using the sound information and the posterior probability for each of the M probability distribution models, the parameters of the current probability distribution model are updated, and updated when it is determined that the parameter values have converged. Output each parameter, and when determining that each parameter value has not converged, an updating unit that causes the parameter holding unit to hold each updated parameter as each parameter of the current probability distribution model, and Prepared,
In the update unit, a direction information distribution estimation device that uses a Dirichlet distribution for the prior distribution of the mixture weight among the parameters. The direction information distribution estimation device according to claim 1,
The probability distribution model is a normal distribution model;
Each parameter of the normal distribution model is a mixture weight, an average, and a variance. The direction information distribution estimation device according to claim 1,
The probability distribution model is a von Mises distribution model;
Each parameter of the von Mises distribution model is a mixture weight, an average, and a diffusion parameter. A frequency domain conversion unit that obtains a frequency domain sound signal by converting sound signals input by a plurality of sound collection means into the frequency domain;
A direction-of-arrival estimation unit for obtaining direction-of-arrival information of sound from the frequency domain sound signal;
A power estimation unit for obtaining power of the frequency domain sound signal;
The direction information distribution estimation device according to any one of claims 1 to 3, wherein a direction information distribution model is obtained using the sound arrival direction information as sound information and the power as a weighting factor.
A sound source number estimation apparatus comprising: a sound source number measurement unit that obtains the number of sound sources by measuring the number M ′ of sound source corresponding direction information distribution models whose mixing weight is larger than a predetermined first threshold. A sound source number estimation apparatus according to claim 4,
A sound source direction measuring device comprising: a sound source direction measuring unit that outputs an average as a sound source direction among parameters of each sound source applicable direction information distribution model. A sound source number estimation apparatus according to claim 4,
A separation unit for obtaining a frequency domain objective signal by obtaining a marginalized posterior probability for each of the M ′ sound source corresponding direction information distribution models, and multiplying the marginalized posterior probability by the frequency domain sound signal;
A sound source separation apparatus comprising: a time domain conversion unit that obtains a target signal by converting the frequency domain target signal into a time domain. Direction information distribution estimation in which one probability distribution model is fitted to each peak using M (M is an integer of 1 or more) probability distribution models when the distribution of sound information from a sound source has a plurality of peaks. A method,
A parameter holding process holding each parameter of the current probability distribution model;
A posteriori probability calculation process of calculating a posteriori probability for each of the M probability distribution models using the sound information and each parameter of the current probability distribution model;
Using the sound information and the posterior probability for each of the M probability distribution models, the parameters of the current probability distribution model are updated, and updated when it is determined that the parameter values have converged. When each parameter is output and it is determined that each parameter value has not converged, there is an update process in which each updated parameter is stored in the parameter holding process as each parameter of the current probability distribution model. And
A direction information distribution estimation method that uses a Dirichlet distribution as a prior distribution of mixture weights among the parameters in the updating process. The direction information distribution estimation method according to claim 7,
The probability distribution model is a normal distribution model;
Each parameter of the normal distribution model is a mixture weight, an average, and a variance. The direction information distribution estimation method according to claim 7,
The probability distribution model is a von Mises distribution model;
The direction information distribution estimation method, wherein each parameter of the von Mises distribution model is a mixture weight, an average, and a diffusion parameter. A frequency domain conversion process for obtaining a frequency domain sound signal by converting a sound signal input by a plurality of sound collecting means into the frequency domain;
A direction-of-arrival estimation process for obtaining sound direction-of-arrival information from the frequency domain sound signal;
A power estimation process for determining the power of the frequency domain sound signal;
Each process of the direction information distribution estimation method according to any one of claims 7 to 9, wherein a direction information distribution model is obtained using the sound arrival direction information as sound information and the power as a weighting factor;
A sound source number estimation method comprising: a sound source number measurement step of obtaining the number of sound sources by measuring the number M ′ of sound source applicable direction information distribution models whose mixing weight is a value larger than a predetermined first threshold. Each process of the sound source number estimation method according to claim 10,
A sound source direction measuring method comprising: a sound source direction measuring process for outputting an average as a sound source direction among parameters of each sound source applicable direction information distribution model. Each process of the sound source number estimation method according to claim 10,
A separation process for obtaining a frequency domain target signal by obtaining a marginalized posterior probability for each of the M ′ sound source corresponding direction information distribution models and multiplying the marginalized posterior probability by the frequency domain sound signal;
A sound source separation method comprising: a time domain conversion process for obtaining a target signal by converting the frequency domain target signal into a time domain.   Each of the direction information distribution estimation method according to claim 7, the sound source number estimation method according to claim 10, the sound source direction measurement method according to claim 11, or the sound source separation method according to claim 12. A program that causes a computer to execute a process.

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