CZ304330B6 - Method of suppressing noise and accentuation of speech signal for cellular phone with two or more microphones - Google Patents

Abstract

The present invention relating to a method of suppressing noise and accentuation of speech signal for cellular phone with two or more microphones is characterized in that a bank (20) of speech suppressing filters is performed for the given cellular telephone in the production or calibration thereof. Each filter of the bank is designed for a concrete position of a speaker relative to the telephone such that output signal thereof has a maximum distance of noise from the speech. The speaker positions relative to the telephone for which the filters are derived in the bank, are selected such as to cover the most probable positions of the speaker. In order to suppress noise and to accentuate the speech signal during a call, the signals of the microphones are filtered in parallel by all the filter of the bank. At a given instant, an output signal is selected as a reference signal from a filter, the output variance of which is minimal. The speech signal is then accentuated by a focuser (40), while the noise is suppressed by an adaptive filter. For the given position of the speaker relative to the telephone, the speech-suppressing filter is derived from a noise-free record of the speaker. The filter is designed such as the variance of its output is minimal, provided the record is an input.

Description

The present invention relates to a method of suppressing noise and interfering sounds (hereinafter referred to as "noise") and enhancing a speech signal in a mobile phone with two or more microphones. This method of noise suppression is based on the use of a pre-measured speech-suppression filter system.

BACKGROUND OF THE INVENTION

The standard method of eliminating unwanted noise using microphone fields is to focus, in English literature referred to as beamforming, to search for a linear combination of microphone field output that maximizes the ratio of useful signal energy and noise energy. This focusing is effective when the geometry of the microphone field - signal source - noise source system does not change over time or does not change rapidly over time, and at the same time if the interference sources are rather point-like. (An alternative to point source noise is diffuse noise, which seems to come from all directions simultaneously.)

An example of unwanted noise suppression by focusing is the method described in US 2012245933 to Flaks et al., January 2010, which includes a variety of techniques and options. A certain similarity to the solution proposed herein is in claim 17, where the possibility of using predetermined focusing parameters for a number of relative positions of the useful signal source (speaker mouth) and the microphone field is mentioned.

Another standard method of suppressing unwanted noise is to use a Wiener filter in the time-frequency domain. This method is suitable for diffuse type noise removal. It needs to estimate the instantaneous frequency spectrum of unwanted noise. This method exists in many different variations, such as appears in S. F. Bolí, Suppression of Acoustic Noise in Speech Using Spectral Subtraction, IEEE Trans. Acoustics, Speech and Signal Processing, vol. 113-120, 1979. In a variant called double spectral subtraction, it appears in the patent by H. Gustafsson, I Claesson and S. Nordholm under US 6,549,586, April 1999. Another variant of spectral subtraction is the PLD (Power Level Differences) algorithm. , proposed by M. Jeub et al., " Noise Reduction for Dual-Microphone Mobile Phones Exploiting Power Level Differences, " IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1693-1696, Kyoto, Japan, March 2012.

In the latter two works it is assumed that the acoustic signal is sensed by two microphones, one microphone oriented towards the mouth of the speaker and the other directed towards the space. The PLD method is based on the assumption that the first microphone captures mainly the speech signal and the second microphone captures unwanted noise.

A limitation of this method is that the speaker's speech signal is partially present on the distant microphone, and although it is typically 10 dB less, it negatively affects the accuracy of the noise spectrum estimation and hence the efficiency of the separation of the useful signal.

A similar concept for a mobile phone with two microphones appears in a more recent patent by H. J. W. Belt et al., US 2007/0230712 A1 of August 11, 2005. Here, it is recognized that the efficiency of spectral readings (Wiener filter) depends on the position of the mobile phone relative to the mouth of the speaker, and therefore it is proposed to add a position detector to the device.

There are other methods of noise suppression and speech enhancement in the dual microphone system in the patent and other scholarly literature, but the above patents and works are for the solution proposed here.

Conceptually closest. Among those other works, for example, Hao Deng, et al., US 2007/0165879 of January 13, 2007 (adaptive filtering method) or Kai Li et al., Two-Microphone Noise Reduction Using Spatial Information-Based Spectral Amplitude Estimation , IEICE Trans, on Information and Systems, vol. 1454-1464, 2012.

It is an object of the present invention to provide a method of suppressing unwanted noise, which is particularly suitable when the noise is diffuse in nature, or is a point source but rapidly changing its position, does not need a mobile phone position detector and is of better quality than known solutions.

SUMMARY OF THE INVENTION

The principle of the method of suppressing noise and enhancing the speech signal in a mobile phone with two or more microphones is that for a given mobile phone (hereinafter referred to as the "telephone") a bank of 10 to 256 speech-suppressing filters of 100 to 1000 ('the Bank'). The input of each filter from the bank is signals from microphones and the output is one signal. Each bank filter is designed for a specific or group of speaker-to-telephone positions so that its output signal contains only noise and suppresses the speaker's voice as much as possible. The speaker-to-telephone positions for which the filters in the bank are derived are selected to cover together the closest neighborhood of the telephone and the most likely speaker-to-telephone positions in normal use. The position and shape of the speaker head, the position and shape of the hand held by the speaker, and the typical noise in the environment where the telephone is used may also be taken into account.

A bank of speech-suppressing filters is created by the learning process after the phone prototype has been constructed. Furthermore, the bank can be customized by the user prior to normal use to calibrate the method to the user's voice, head shape and manner of holding the phone, and possibly to the most common environmental noise. For a given position or group of positions of the speaker relative to the telephone, the position and shape of his or her head and the position and shape of his hand (hereinafter referred to as the "situation"), the filter is derived by recording . The records shall be such that they contain only a negligible amount of noise. The filter is designed so that the variance of its output, if its input is these records, is minimal and, at the same time, so that the transmitted noise has the least altered spectrum.

To suppress the noise and enhance the speech signal during a call, the microphone signals are filtered in parallel by all bank filters and the signal variation at their output is measured. At the moment, the output signal from the filter whose output variance is minimal is selected as the noise reference signal. Noise suppression and enhancement of the speech signal in a given microphone signal or a signal that is a mixture of microphone signals (the "guaranteed signal") is accomplished by subtracting noise from the focusing output signal by an adaptive Wiener filter. Another variation of the subtraction is the double spectral subtraction proposed in the aforementioned U.S. Patent No. 6,549,586. A third subtraction method is the PLD algorithm proposed in Jeuba et al., ICASSP 2012, also cited above. Finally, for all of these spectral subtraction methods, listening quality can be improved by smoothing (averaging) the spectrogram at higher frequencies, as described in P. Echt and P. Vary, Effective musical noise suppression for speech enhancement systems, Proceedings of IEEE Int, Conference on Acoustics, Speech and Signal Processing, ICASSP, Taipei, Taiwan, 2009 4409 - 4412

BRIEF DESCRIPTION OF THE DRAWINGS

Giant. 1 is a block diagram of a method of suppressing noise and enhancing a speech signal in a mobile phone that may be used in any embodiment of the invention.

Giant. 2 is a representation of typical positions from which pure speaker recordings are taken to calculate and derive a bank of speech-suppressing filters.

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DETAILED DESCRIPTION OF THE INVENTION

Example 1: Creating a Speech-Suppressing Filter Bank

A bank of speech-suppressing filters is created by the learning process after the phone prototype has been constructed. First, the phone records the speaker from different positions in relation to the phone. The number of recordings is 50, the length of each record is 5 seconds, and each record contains the spoken words of the person to be recorded "one, two, three, four. Speaker positions relative to the phone are selected to cover together the closest neighborhood of the phone use (see Fig. 2). Recordings are made in a quiet environment where the noise level is below 40 dB.

In addition, a 5-second noise recording is performed, typical of the phone's environment.

For each speaker recording, a speech-suppression filter is calculated, which is determined for the p-th recording by the vectors g _{pL and} g, _p , _R containing filter coefficients and each having a length of 300 according to the formula [gp, _L g _P , _R 1 = argmin £ {| {g _L x x _pL } (n) + {g _R x x _{p> R} } (n) f +

Bl'Er not | _{L * V g} _L (n) + g _{R} y _R (n) - y _L (n - D) | ² }, where x _p , [. (N) and x _{P) R} (n) denote samples of the pth recording of pure speech, y _L (n) and VR (n) denote samples of the above-mentioned noise recording, * denotes a convolution operation, and, the regularization constant is 0.1 and D is the integer noise delay constant equal to 20. The purpose of the second term in the formula is that the speech suppression filter does not change the spectrum of the noise it transmits too much. The task of minimization is transformed into a system of linear equations with a block toeplitz matrix, which is quickly and economically solved by a block Levinson-Durbin algorithm, derived by H. Akaike, "Block Toeplitz Matrix Inversion", SIAM J. Appl. Math. 24 (2): 234-241 (1973).

Calculated speech-suppressing filters, more precisely the FFT transformation of zero-extended filters to block lengths, form a filter bank that is stored in the phone memory.

Example 2: A simpler variant of creating a bank of speech-suppressing filters

The procedure is the same as in Example 1 except that the noise recording y _L (n) and yu (n) are not needed and the speech-suppression filter is calculated for the given speaker recording using the formula g _pL = argmin ^ | {g _L * x _pL } (n) - x _pR (nD) | ² .

bl _n In this case, the coefficients g _p> R are all zero except D-th, which is equal to -1 and g _p , so _R is the same for all speech-suppressing filters (for all p). The task of minimization is transformed into a system of linear equations and is solved by the Levinson Durbin algorithm.

This simpler version of calculation of speech-suppression filters is less computationally demanding and has less memory requirements (it is enough to store the coefficients g _p , L). However, it does not allow adaptation to the type of noise removed and the speaker signal suppression is weaker.

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Example 3: Performing a method of suppressing noise and enhancing a speech signal

Fig. 1 is a block diagram of a method of suppressing noise and enhancing a speech signal in a mobile phone that may be used in any embodiment of the invention.

The signals from the microphones sampled at 16 kHz x _L ( ⁿ ) ^and x _R (n), where n is the sample index, are first transformed by a fast window Discrete Fourier Transform (window FFT) 10 where the window length is 1024 samples and the window overlay is 50 %. The block (window) of the transformed signals is denoted X _L (k) and X _R (k), where k is the frequency band index.

X _L (k) and X _R (k) are input to the bank 20 of speech-suppressing filters, where they are parallel filtered by all filters from the bank. The filter coefficients are read from the phone memory. The output of the p-th filter is calculated according to the formula Zp (k) = Gpj (k) -X | (k) + G ₍₎ , _R (kpX _R (k), where G _p , _L (k) and G _PiR (k) ) are the FFT transform coefficients of the p-th filter.

The filter selector 30 evaluates the output variations of the speech suppressing filters. The variance of the p-th filter ςμ ^! Calculated according to the formula k The output of the filter selector 30 denoted by Z (k) is the output of the filter whose variance is the smallest. If it is the p-th filter, then Z (k) = Z _p (k).

In parallel to the calculation of the signal Z (k), the focuser 40, which is inputted by the signals X _L (k) and X _R (k), calculates the signal X (k), which is the input to the noise subtractor 50. The aim is to increase the speech-to-noise ratio by means of a focuser 40 which can be selected according to the manner in which the microphones are placed on the telephone. If the microphones are positioned both in front, one of the known focusers 40 may be used, such as a delay-and-sum beamformer or set X (k) equal to the signal from the microphone whose variance is higher. If the microphones are placed one at the front (signal X _L (k)) and the other at the rear (signal X _R (k)), then X (k) is equal to X _L (k).

The noise reader 50 reads the Z (k) signal from the X (k) signal and outputs the S (k) signal. Metho | x (k) | used ²

S (k) = | x (k) | ² + τ (Κ) | ²

X (k), dou's subtraction is an adaptive Wiener filter, implemented by the formula where τ is an optional parameter equal to 10, which controls the amount of noise suppression. To maintain the listening quality of the signal, the formula is used only for index values k from 0 to K, where K corresponds to a frequency of 3 kHz. For values k> K then S (k) = X (k).

The inverse FFT transformation 60 and the OLA method convert the S (k) signal into the time domain s (k) using the inverse FFT and the overlap-add (OLA) method, which is described, for example, in B. Porat, "A Course in Digital Signal Processing" , John Wiley & Sons, Inc., 1997.

Industrial applicability

The invention is designed to be implemented in mobile phones having two or more microphones to capture sound. The invention is intended to facilitate telephony in noisy environments by suppressing environmental noise and amplifying the caller's speech signal in a transmitted telephone call.

Claims (3)

A method for suppressing noise and enhancing a speech signal for a mobile phone with two or more microphones, characterized in that the noise component is estimated by means of a speech suppression filter bank (20) and a filter selector (30), wherein the selector (30) The filter of the filter always selects the output signal from the filter whose output variance is minimal, and the actual speech signal estimation is performed in the noise subtraction (50) by subtracting the estimated noise component from the focusing signal (40) by one of the known spectral subtraction methods. is a Wiener filter, or derived methods such as Power level difference. The method of claim 1, wherein the speech suppression filter bank (20) is formed for an existing mobile phone prototype based on a set of speaker recordings holding the phone at various positions as expected in normal use of the phone, per g _p. , _L = argmin ^ | {g _L * x _pL } (n) - x _pR (nD) | ² by minimizing the expression ^gL n denotes samples of the fifth recording of pure speech from both microphones, n is a time index, * is a convolution operator, and D is a delay parameter. The method of claim 1, wherein the speech suppression filter bank (20) is formed for an existing mobile phone prototype based on a set of speaker recordings holding the phone prototype at various positions as expected in normal use of the phone and audio. recording noise (noise) environment, which is assumed to be typical to use the phone, based on the minimization of the expression [g _P l g _L, R] = argmin £ {| {g _L * x _pL} (n) + { g _R * x _{p R} } (n) f + Bl <Br ns | {Y _L * g} _L (n) + g _{R} * _R Y (n) - _yl (n - D) | ² }, where Xp, i (n) and x _p , R (n) denote samples of the pth record of pure speech by both microphones, y _L (n) and yR (n) denote samples of the aforementioned noise record, n is a time index, * is the convolution operator, D is the delay parameter and ε is the regularization constant.