System and method for adaptive multi-sensor arrays

An adaptive differential microphone array method comprises receiving a signal, estimating a measured signal spectral covariance matrix of the signal, and estimating a direction of arrival of the signal based on the measured signal spectral covariance matrix of the signal. The method further comprises determining a fractional delay, and applying a differential microphone array filter to the signal based on the direction of arrival and the fractional delay.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to signal processing, and more particularly to an adaptive multi-sensor array and method thereof.

2. Discussion of the Related Art

Microphone arrays are used in various devices, such as hearing aids and speech recognition applications. One problem with these devices is noise, and the elimination of noise from a signal received by the microphone array. In the field of hearing aids, there exist several proposed solutions. The proposed solutions can be divided into three classes of microphone array schemes: side-lobe canceller, beamformer, and differential microphone array (DMA). Many of the known adaptive methods for reducing noise address the first two classes, side-lobe cancellers and beamformers. However, few works address the adaptability of the differential microphone array scheme.

Two works that discuss differential microphone arrays are H. Teutsch and G. W. Elko, “First and second-order adaptive differential microphone arrays”, 7thInternational Workshop on Acoustic Echo and Noise Control, pages 35–38, 2001; and U.S. Pat. No. 5,473,701 to J. Cezanne and G. W. Elko entitled “Adaptive Microphone Array”. The differential microphone array works implement an adaptation by minimizing an output variance.

The proposed solutions implementing differential microphone arrays utilize an optimization criterion, which is a total output power or energy. The criterion is of the form T1+T2where T1is the output energy due to the signal, and T2the output energy due to the noise. Thus, the optimization problem is: min(T1+T2)

This optimization problem has an inherent limitation; when the source position is in a null-allowed region, then either no processing is done, or if the optimization problem is solved, it cancels a target source.

Similarly, a directivity index of the prior art differential microphone array work is always maximal for theta=0. Thus, it is an end-fire array and there is no adaptability when the source moves.

Additionally, a parameter beta is implemented that adjusts the directivity pattern.

Therefore, a need exists for an adaptive multi-sensor array.

SUMMARY OF THE INVENTION

According to an embodiment of the present invention, an adaptive differential microphone array method comprises receiving a signal, estimating a measured signal spectral covariance matrix of the signal, and estimating a direction of arrival of the signal based on the measured signal spectral covariance matrix of the signal. The method further comprises determining a fractional delay, and applying a differential microphone array filter to the signal based on the direction of arrival and the fractional delay.

The signal is a sample.

Determining the fractional delay comprises obtaining the fractional delay from a look-up table based on the direction of arrival through a maximization of a weighted directivity index, and applying the fractional delay in the differential microphone array filter.

According to an embodiment of the present invention, an adaptive differential microphone array method comprises receiving a signal, and estimating a measured signal spectral covariance matrix of the signal, and estimating a direction of arrival of the signal. The method further comprises determining a presence of a source of interest in the signal based on the direction of arrival of the signal, a noise spectral covariance matrix of the signal, and a source signal spectral power of the signal, estimating the noise spectral covariance matrix based on a determination of the presence of the source of interest and the measured signal spectral covariance matrix of the signal. The method comprises estimating a source signal spectral power of the signal according to the presence of the source of interest the noise spectral covariance matrix and the noise spectral covariance matrix, and applying a differential microphone array filter to signal according to the direction of arrival, the noise spectral covariance matrix, and the source signal spectral power of the signal.

The signal is a sample.

The method further comprising determining a fractional delay, and applying the fractional delay in the differential microphone array filter.

Determining the two fractional delays further comprises determining (τ1,τ2)=argmaxd1,d2I(d1,d2).

According to an embodiment of the present invention, a digital processor for reducing a noise portion of a signal comprises means for estimating a measured signal spectral covariance matrix of the signal, means for estimating a direction of arrival of the signal, and a differential microphone array filter.

The digital processor further comprises a voice activity detector outputting a signal indicating the presence of a source of interest in the signal, means for estimating a noise spectral covariance matrix based on the signal indicating the presence of a source of interest in the signal and the measured signal spectral covariance matrix, and means for estimating a source signal spectral power of the signal according to the noise spectral covariance matrix, and the measured signal spectral covariance matrix.

According to an embodiment of the present invention, a program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for an adaptive differential microphone array method. The method steps comprising receiving a signal, estimating a measured signal spectral covariance matrix of the signal, and estimating a direction of arrival of the signal based on the measured signal spectral covariance matrix of the signal. The method further comprising determining a fractional delay, and applying a differential microphone array filter to the signal based on the direction of arrival and the fractional delay.

According to an embodiment of the present invention, a program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for an adaptive differential microphone array method. The method steps comprise receiving a signal, estimating a measured signal spectral covariance matrix of the signal, and estimating a direction of arrival of the signal. The method further comprises determining a presence of a source of interest in the signal based on the direction of arrival of the signal, a noise spectral covariance matrix of the signal, and a source signal spectral power of the signal, and estimating the noise spectral covariance matrix based on a determination of the presence of the source of interest and the measured signal spectral covariance matrix of the signal. The method comprises estimating a source signal spectral power of the signal according to the presence of the source of interest the noise spectral covariance matrix and the noise spectral covariance matrix, and applying a differential microphone array filter to signal according to the direction of arrival, the noise spectral covariance matrix, and the source signal spectral power of the signal.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

According to an embodiment of the present invention, a system and method for adaptive array signal processing for multi-sensor arrays estimates a direction of arrival (DOA) of a main source. Upon determining an estimated direction of arrival of the main source, a differential microphone array (DMA) is adapted using parameters taken from a predetermined look-up table. According to an embodiment of the present invention, the differential microphone array filter parameters are tuned using an estimate of the direction of arrival of the main source, an estimate of a source spectral power, and an estimate of a noise field spectral covariance matrix. Thus, an improved recorded audio signal is obtained as compared to a recording made using one microphone.

According to an embodiment of the present invention, an adaptive multi-sensor array can be implemented in a hearing aid. However, adaptive multi-sensor arrays are not limited to hearing aids, and can be applied to any microphone array based audio processing system, such as for improving the quality of a voice in a car environment, or the quality of a patient voice while in a gantry of a CT scanner.

An estimate of the direction of arrival of a dominant source is obtained, as well as the source spectral power, and the noise field spectral covariance matrix. Based on the estimates of the direction of arrival of the dominant source, the source spectral power, and the noise field spectral covariance matrix a differential microphone array scheme can be adapted to reduce a noise level in a recorded signal.

Referring toFIG. 1, a direction of arrival based adaptive differential microphone array method is shown. Referring toFIG. 2, a voice activity detector (VAD) and direction of arrival based adaptive differential microphone array method is depicted.

As shown inFIG. 1, in the direction of arrival based adaptive differential microphone array, the differential microphone array filter coefficients are updated from a look-up table based on the current estimate of direction of arrival. In the voice activity detector and direction of arrival based adaptive differential microphone array shown inFIG. 2, the differential microphone array filter coefficients are obtained as optimizers of a modified directivity index that takes into account the estimated source direction of arrival, as well as the current estimate of the noise spectral covariance matrix and source spectral power.

A system for implementing the methods ofFIG. 1andFIG. 2is shown inFIG. 3. Referring toFIG. 3, the array of microphones301converts an acoustic pressure into an electric signal. The electric signal is amplified by amplifiers A302and converted into a digital domain by the analog-to-digital (A/D) converters303. The digitized measured signals can be denoted by x1, . . . xD, where D is the number of microphones. The signal in the digital domain is processed by a digital processor304, described with respect toFIGS. 1 and 2. A processed signal is converted back into the analog domain by a digital-to-analog (D/A) converter305. The signal is amplified by amplifier306and converted into a pressure wave by the loudspeaker307.

It is to be understood that the present invention may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. In one embodiment, the present invention may be implemented in software as an application program tangibly embodied on a program storage device. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture.

Referring toFIG. 4, according to an embodiment of the present invention, a computer system401for implementing the present invention can comprise, inter alia, a central processing unit (CPU)402, a memory403and an input/output (I/O) interface404. The computer system401is generally coupled through the I/O interface404to a display405and various input devices406such as a mouse and keyboard. The support circuits can include circuits such as cache, power supplies, clock circuits, and a communications bus. The memory403can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combination thereof. The present invention can be implemented as a routine407that is stored in memory403and executed by the CPU402to process the signal from the signal source408. As such, the computer system401is a general purpose computer system that becomes a specific purpose computer system when executing the routine407of the present invention.

The computer platform401also includes an operating system and micro instruction code. The various processes and functions described herein may either be part of the micro instruction code or part of the application program (or a combination thereof) which is executed via the operating system. In addition, various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device.

Referring toFIG. 1, the direction of arrival based adaptive differential microphone array method comprises three blocks: estimation of the measured signal spectral covariance matrix Rx101, estimation of direction of arrival102, and differential microphone array filter, Filter1103.

The estimation of the measured signal spectral covariance matrix Rx101comprises initializing Rxfor every frequency. The initialization can be written as:

Rx⁡(ω)=0,for⁢⁢M⁢⁢values⁢⁢of⁢⁢ω,ω∈S={0,2⁢⁢πM,⁢…⁢,2⁢⁢π⁡(M-1)M}
where M is a number of samples. The initialization can be, for example, to zero (0). For each input, X, a windowed Fourier transform is determined. For example, for a three-microphone array, the windowed Fourier transform can be determined using the last M samples
x1(t),x1(t−1), . . . ,x1(t−M+1),x2(t),x2(t31 1), . . . ,x2(t−M+1),x3(t),x3(t−1), . . . ,x3(t−M+1)
where the transform is written as:

Xx⁡(ω)=∑l=0M-1⁢w⁡(l)⁢x⁡(t-1)⁢ⅇ-ⅈ⁢⁢wl,k=1,2,3,ω=0,2⁢⁢πM,⁢…⁢,2⁢⁢π⁡(M-1)M
where w(0), . . . , w(M−1) is the window, and for every ωεS (ω is called frequency). Rxis updated at every frequency. The updated Rxis based on a previous frame and a present frame.

Various direction of arrival estimation techniques can be used. For example, set τ=dfs/c, where d is the distance between two adjacent microphones, fsis the sampling frequency, and c is the speed of sound. For k=−N,−N+1, . . . , N determine

A fractional delay, {circumflex over (δ)}, and the direction of arrival estimate are determined as:

δ^=kmax⁢τNθ^=arccos⁡(kmaxN)
The fractional delay measures the time it takes between when a sound arrives at a first microphone and when the sound arrives at a second microphone.

The differential microphone array filter is used to determine the measured signals for each input, xD. According to an embodiment of the present invention, the differential microphone array uses parameters τ1,τ2obtained from a look-up table. The look-up table determines delays d1,d2for each estimated direction of arrival. The look-up table gives a list of τ1,τ2in an optimization cost criterion J(d1,d2).

The parameters τ1,τ2, e.g., optimized values of d1,d2, depend on the direction of arrival through a maximization of a weighted directivity index. The directivity index suppresses certain directions and amplifies others. Suppressed directions include those directions from which noise originates. Amplified directions include those with a source of interest.

An aligned signal is determined based on two or more signals received at corresponding sensors. The two or more signals are aligned using the values for fractional delays τ1, τ2. The parameters τ1,τ2depend on the direction of arrival through a maximization of a weighted directivity index.

The optimization cost criterion J(d1,d2) be given by a weighted average of frequency dependent normalized signal-to-noise ratios is determined. The optimization cost criterion takes into account where ν=[1−eiωd1−eiωd2eiω(d1+d2)]{circumflex over (δ)} obtained in the direction of arrival estimation, a weight, e.g., an articulation index, and a matrix for determining a spectral power of noise in a diffuse noise environment. The parameters τ1,τ2are obtained as:
(τ1, τ2)=argmaxd1,d2J(d1,d2).

Referring toFIG. 2, the voice activity detector and direction of arrival based adaptive differential microphone array method comprises an estimation of Rx201, an estimation of the direction of arrival202, a voice activity detector203, an estimation of Rn204, an estimation of Rs205, and a differential microphone array filter, Filter2206.

Known voice activity detectors can be used. The voice activity detector discriminates between speech and non-speech, and can discard non-speech portions of a signal. The voice activity detector203outputs a binary signal: 0 or 1, 0 when a source of interest is not present, and 1 when a source of interest is present.

Based on the voice activity detector decision as to the presence of a source sound, the noise spectral covariance matrix Rn204is adapted at every frequency. For example, as:

Rnupdated⁡(ω)={Rnprevious⁡(ω)ifVAD=1(1-β)⁢Rnprevious⁡(ω)+β⁢⁢X⁡(ω)⁢X*⁡(ω)ifVAD=0
where β is the noise learning rate, and X(ω) is the vector X=[X1X2X3]Tof the components in the windowed Fourier transform.

The source signal spectral power Rs205is estimated by a spectral subtraction method. For example:

Rs={Rx;11-Rn;11ifRx;11≥βSS⁢Rn;11(βSS-1)⁢Rn;11ifotherwise
where βSS>1 is a coefficient. Various spectral subtraction methods can be used, these are known in art, for example, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP 27, No. 2, April 1979, Steven Boll, Suppression of Acoustic Noise in Speech Using Spectral Subtraction, pp. 113–120.

The differential microphone array filter206, Filter2, Filters the measured signals xD. For example, for the three-microphone array, the measured signals x1,x2, x3are filtered by the differential microphone array filter using the aligned signal. According to an embodiment of the present invention, τ1,τ2are obtained as the solution of the optimization problem:
(τ1,τ2)=argmaxd1,d2I(d1,d2)
where the criterion I(d1, d2) takes into account the following variables: ν=[1−eiωd1−eiωd2eiω(d1+d2)], {circumflex over (δ)} is obtained in the direction of arrival estimations, Rs(ω) is a signal spectral power, and Rns(ω) is a noise spectral covariance matrix. This criterion represents a generalization of the directivity index.

Referring again toFIG. 3, a system according to an embodiment of the present invention comprises an array of close-by microphones301; the distance between any two adjacent microphones is about 1 cm. Amplifiers302and A/D converters303are provided in series, one of each for each microphone. A digital processor304is coupled to the A/D converters303. A D/A converter305is coupled to the digital processor304. An amplifier306and loudspeaker307can be provided, coupled in series from the D/A converter305.

The direction of arrival estimation can be done less frequently than the differential microphone array filtering; more specifically, the differential microphone array filtering is done at the sampling rate of the digitized signal. The frequency of the direction of arrival estimation depends on the processing power of the digital processor, and can be done after every M samples, or after a longer time. Fractional delay parameters of the differential microphone array filter are determined on-line and the solutions for the fractional delay parameters for each estimated direction of arrival are stored in a look-up table. The aligned signal is determined less frequently than the differential microphone array filtering. As in the direction of arrival based adaptive differential microphone array scheme, in the voice activity detector and direction of arrival based adaptive differential microphone array scheme the differential microphone array filtering is done at the sampling rate, whereas the direction of arrival estimation and voice activity detector decision depend on the processing power of the digital processor, and can be done after every M samples, or after a longer time. The aligned signal is determined on a grid in d1,d2parameters space with a resolution depending on the processing power available; a step size is about 0:01 per sample. The fractional delays are implemented by FIR (Finite Impulse Response) filters.

The differential microphone array can be implemented in the filter bank approach.

Having described embodiments for an adaptive multi-sensor array and method, it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments of the invention disclosed which are within the scope and spirit of the invention as defined by the appended claims. Having thus described the invention with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.