Patent Application
20110029309
The present invention relates to a signal separating apparatus and a signal separating method that extract a specific signal in the state where a plurality of signals are mixed in a space and, particularly to permutation solving technology.
Recently, a technique of extracting only user speech in hands-free by using microphone array has been developed. In a system to which such speech extraction technique is applied, it is necessary to suppress such noise in order to recognize the user speech correctly, because uttered speech (interference sound) other than the user speech to be extracted and diffusive noise called ambient noise are generally mixed in the user speech.
As a processing technique for suppressing noise, frequency domain independent component analysis is effective for use that assumes that sound sources are independent, applies learning rule for filtering in the frequency domain, and separates sound sources. In this technique, filters should be classified as a filter designed for extracting sound source of user speech or noise because the filter is designed in each frequency band. Such classifying is called “solution of the permutation (transpose) problem”. When the solution is failed, even if user speech to be extracted and noise are appropriately separated in each frequency band in the independent component analysis, a sound with a mixture of user speech and noise is eventually output.
For example, a technique related to the solution of the permutation problem is proposed in Patent Document 1. In the system disclosed in this document, short-time Fourier transform is performed on observed signals, separating matrixes are obtained at each frequency by the independent component analysis, the arrival directions of the signals extracted from each row of the separating matrixes at each frequency are estimated, and it is determined whether the estimated values are reliable enough. Further, the similarity of separated signals between frequencies is calculated, and separating matrixes are obtained at each frequency, and, after that, the permutation is solved.
In the technique of solving the permutation problem disclosed in Patent Document 1, it is assumed that noise is a point sound source which is emitted from a single point, and classifying is performed on the basis of the source angles estimated in each frequency band. However, in the case of diffusive noise, because the direction of the noise cannot be identified, estimation errors in the classifying become larger, and a desired operation cannot be performed in spite of the similarity calculation in the subsequent stage.
The present invention has been accomplished to solve the above problems and an object of the present invention is thus to provide a signal separating apparatus and a signal separating method that can correctly solve the permutation problem and separate user speech to be extracted.
A signal separating apparatus according to the present invention is a signal separating apparatus that separates a specific speech signal and a noise signal from a received sound signal, which includes a signal separating unit that separates at least a first signal and a second signal in the sound signal, a joint probability density distribution calculation unit that calculates joint probability density distributions of the first signal and the second signal separated by the signal separating unit, and a classifying determination unit that determines the first signal and the second signal as the specific speech signal or the noise signal based on shapes of the joint probability density distributions calculated by the joint probability density distribution calculation unit.
The classifying determination unit preferably determines a signal with a non-Gaussian shape of the joint probability density distribution as the specific speech signal and determines a signal with a Gaussian shape as the noise signal.
It is also preferred that the classifying determination unit discriminates between the specific speech signal and the noise signal based on distribution widths in the shapes of the joint probability density distributions.
It is further preferred that the classifying determination unit discriminates between the specific speech signal and the noise signal based on distribution widths at a frequent value determined on basis of a most frequent value in the shapes of the joint probability density distributions.
Further, the signal separating unit preferably separates the first signal and the second signal for each of a plurality of frequencies contained in the received sound signal.
A robot according to the present invention includes the above-described signal separating apparatus, and a microphone array composed of a plurality of microphones that supply sound signals to the signal separating apparatus.
A signal separating method according to the present invention is a signal separating method that separates a specific speech signal and a noise signal from a received sound signal, which includes a step of separating at least a first signal and a second signal in the sound signal, a step of calculating joint probability density distributions of the first signal and the second signal, and a step of determining the first signal and the second signal as the specific speech signal or the noise signal based on shapes of the calculated joint probability density distributions.
It is preferred that a signal with a non-Gaussian shape of the joint probability density distribution is determined as the specific speech signal, and a signal with a Gaussian shape is determined as the noise signal.
It is also preferred that the specific speech signal and the noise signal are discriminated based on distribution widths in the shapes of the joint probability density distributions.
It is further preferred that the specific speech signal and the noise signal are discriminated based on distribution widths at a frequent value determined on basis of a most frequent value in the shapes of the joint probability density distributions.
Further, it is preferred that the first signal and the second signal are separated for each of a plurality of frequencies contained in the received sound signal.
According to the present invention, it is possible to provide a signal separating apparatus and a signal separating method that can correctly solve the permutation problem and separate user speech to be extracted.
First, the overall configuration and processing of a signal separating apparatus according to an embodiment of the present invention are described with reference to the block diagram of
As shown therein, a signal separating apparatus 10 includes an analog/digital (A/D) conversion unit 1, a noise suppression unit 2, and a speech recognition unit 3. A microphone array composed of a plurality of microphones M1 to Mk is connected to the signal separating apparatus 10, and sound signals detected by the respective microphones are received to the microphone apparatus 10. The signal separating apparatus 10 is incorporated into a guide robot placed in a show room or an event site or other robots, for example.
The A/D conversion unit 1 converts the respective sound signals received from the microphone array M1 to Mk into digital signals, which are sound data, and outputs the data to the noise suppression unit 2.
The noise suppression unit 2 executes process of suppressing noise contained in the received sound data. As shown in the figure, the noise suppression unit 2 includes a discrete Fourier transform unit 21, an independent component analysis unit 22, a gain correction unit 23, a permutation solving unit 24, and an inverse discrete Fourier transform unit 25.
The discrete Fourier transform unit 21 executes discrete Fourier transform for each of the sound data corresponding to the respective microphones and identifies the time series of the frequency spectra.
The independent component analysis unit 22 performs independent component analysis (ICA) based on the frequency spectra received from the discrete Fourier transform unit 21 and calculates separating matrixes at each frequency. Specific processing of the independent component analysis is disclosed in detail in Patent Document 1, for example.
The gain correction unit 23 executes gain correction process on the separating matrixes at each frequency calculated by the independent component analysis unit 22.
The permutation solving unit 24 executes process for solving the permutation problem. Specific processing is described in detail later.
The inverse discrete Fourier transform unit 25 executes inverse discrete Fourier transform and converts the frequency domain data into time domain data.
The speech recognition unit 3 executes speech recognition process based on the sound data whose noise is suppressed by the noise suppression unit 2.
The configuration and processing of the permutation solving unit 24 are described hereinafter with reference to the block diagram of
The joint probability density distribution estimation unit 241 calculates joint probability density distributions of the separated signals at each frequency and calculates their joint probability density distributions.
The classifying determination unit 242 determines classifying on the basis of the shapes of the joint probability density distributions estimated by the joint probability density distribution estimation unit 241. Specifically, the classifying determination unit 242 determines whether the joint probability density distribution shape is a non-Gaussian signal which is specific to user speech or a Gaussian signal of noise over a wide range.
In actual processing, the classifying determination unit 242 calculates the value of the distribution width when the frequent value is reduced from the maximum value at a constant rate in the joint probability density distribution is calculated for each of the separated signals. Then, comparing those distribution widths, it determines the separated signal which is determined to have a small distribution width as user speech and determines the one with a large distribution width as noise.
The process of solving the permutation problem is specifically described hereinafter with reference to the flowchart of
First, the independent component analysis unit 22 or the like creates a separated signal group Y1 (f, m) composed of a plurality of separated signals (S101). Note that 1 is a group number, f is a frequency-bin, and m is a frame number. Next, the joint probability density distribution estimation unit 241 of the permutation solving unit 24 determines whether there is an undetermined frequency-bin (S102). When, as a result of the determination, the joint probability density distribution estimation unit 241 determines that there is an undetermined frequency-bin, it selects f0 from the undetermined frequency-bin (S103).
Then, the joint probability density distribution estimation unit 241 calculates the joint probability density distribution of the separated signal group Y1 (f0, m) with the frequency f0 (S104). Next, the classifying determination unit 242 extracts features (non-Gaussian characteristic) from the shape of the calculated joint probability density distribution of the separated signal group Y1 (f0, m) with the frequency f0 (S105).
Based on the extracted features, the classifying determination unit 242 determines a signal with the highest non-Gaussian characteristic as speech Y1 (f0, m) and the other signal as noise Y2 (f0, m) (S106). After that, the process returns to the processing of Step S102.
When it is determined in Step S102 that there is no undetermined frequency-bin, speech Y1 (f, m) and noise Y2 (f, m) indicating a result of classifying into user speech or noise at each frequency are output.
Results of verifying a signal separating method according to the embodiment are described hereinafter with reference to
As described above, the signal separating apparatus according to the embodiment makes determination of the classifying on the basis of the shapes of the joint probability density distributions of the separated signals and is thus capable of accurately identifying which cluster the user speech is.
The present invention is applicable to a signal separating apparatus and a signal separating method that extract a specific signal in the state where a plurality of signals are mixed in a space and, particularly to permutation solving technology.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2008-061727 | Mar 2008 | JP | national |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/JP2008/065717 | 9/2/2008 | WO | 00 | 9/10/2010 |
