# US 5,706,395 A · Adaptive weiner filtering using a dynamic suppression factor

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- Number: US 5,706,395 A
- Document ID: US5706395A
- Status: Granted
- Kind: A
- Application number: 08/425,125
- Filed: 1995-04-19
- Granted: 1998-01-06
- Earliest claimed: 1995-04-19
- Assignees: Texas Instruments Incorporated
- Inventors: Levent M. Arslan; Alan V. McCree; Vishu R. Viswanathan
- Web page: https://patentgrep.com/patent/US5706395A

## CPC

- G10L 21/0208: Noise filtering
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

An acoustic noise suppression filter including attenuation filtering with a noise suppression factor depending upon the ratio of estimated noise energy of a frame divided by estimated signal energy.

## Claims

### Claim 1

A filter, comprising:
(a) an input for receiving frames of sampled signals;
(b) an attenuation filter coupled to said input, wherein said attenuation filter includes a noise suppression factor with said noise suppression factor depending on E.sub.N divided by E.sub.Y where E.sub.N is an estimate of noise energy of a frame and E.sub.Y is an estimate of signal energy of said frame; and
(c) an output coupled to said attenuation filter for emitting filtered frames.

### Claim 2 (depends on 1)

The filter of claim 1, wherein:
(a) said noise suppression factor is proportional to E.sub.N /E.sub.Y.

### Claim 3 (depends on 1)

The filter of claim 1, wherein:
(a) said attenuation filter has a transfer function H(.omega.) given by H(.omega.).sup.2 =P.sub.Y (.omega.)/(P.sub.Y (.omega.)+›E.sub.N /(E.sub.Y -E.sub.N !.kappa.P.sub.N (.omega.)) where P.sub.Y is an estimate of the signal power spectrum of said frame, P.sub.N is an estimate of the noise power spectrum of said frame, and .kappa. is a constant.

### Claim 4 (depends on 3)

The filter of claim 3, wherein:
(a) .kappa. is in the range of 6 to 7.

### Claim 5 (depends on 3)

The filter of claim 3, wherein:
(a) P.sub.Y is the power spectrum of a linear prediction coefficient (LPC) approximation of said frame; and
(b) E.sub.Y is the energy of said LPC approximation.

### Claim 6 (depends on 3)

The filter of claim 3, wherein:
(a) said P.sub.N is taken equal to: (i) a first product of a first constant and a noise power spectrum estimate for a preceding frame when P.sub.Y exceeds said first product, (ii) a second product of a second constant and said noise power spectrum estimate for a preceding frame when P.sub.Y is less than said second product, and (iii) P.sub.Y otherwise; and
(b) said E.sub.N is the sum over frequencies of said P.sub.N.

### Claim 7 (depends on 1)

The filter of claim 1, wherein:
(a) said attenuation filter has a transfer function H(.omega.) given by H(.omega.).sup.2 =max{C, P.sub.Y (.omega.)/(P.sub.Y (.omega.)+›E.sub.N /(E.sub.Y -E.sub.N)!.kappa.P.sub.N (.omega.))} where max{A,B} is the maximum of A and B for all A and B, C is a clamp, P.sub.Y is an estimate of the signal power spectrum of said frame, P.sub.Y is an estimate of the noise power spectrum of said frame, and .kappa. is a constant.

### Claim 8 (depends on 7)

The filter of claim 7, wherein:
(a) said P.sub.N is taken equal to: (i) a first product of a first constant and a noise power spectrum estimate for a preceding frame when P.sub.Y exceeds said first product, (ii) a second product of a second constant and said noise power spectrum estimate for a preceding frame when P.sub.Y is less than said second product, and (iii) P.sub.Y otherwise; and
(b) said E.sub.N is the sum over frequencies of said P.sub.N.

### Claim 9 (depends on 1)

The filter of claim 1, wherein:
(a) said attenuation filter has a transfer function H(.omega.) given by H(.omega.).sup.2 =W.star-solid.max{C, P.sub.Y (.omega.)/(P.sub.Y (.omega.)+›E.sub.N /(E.sub.Y -E.sub.N)!.kappa.P.sub.N (.omega.))} where W is a window function, .star-solid. denotes convolution, max{A,B} is the maximum of A and B for all A and B, C is a clamp, P.sub.Y is an estimate of the signal power spectrum of said frame, P.sub.N is an estimate of the noise power spectrum of said frame, and .kappa. is a constant.

### Claim 10

A method of filtering a stream of sampled acoustic signals, comprising the steps of:
(a) partitioning a stream of sampled acoustic signals into a sequence of frames;
(b) Fourier transforming said frames to yield a sequence of transformed frames;
(c) applying a generalized Wiener filter with a noise suppression factor to said transformed frames to yield a sequence of filtered transformed frames, wherein said noise suppression factor of said filter for a transformed frame depends upon estimates of the signal-to-noise ratio of said transformed frame; and
(d) inverse Fourier transforming said sequence of filtered transformed frames to yield a sequence of filtered frames.

### Claim 11 (depends on 10)

The method of claim 10, wherein:
(a) said generalized Wiener filter has a transfer function H(.omega.) given by H(.omega.).sup.2 =max{C, P.sub.Y (.omega.)/(P.sub.Y (.omega.)+›E.sub.N /(E.sub.Y -E.sub.N)!.kappa.P.sub.N (.omega.))} where max{A,B} is the maximum of A and B for all A and B, C is a clamp, P.sub.Y is an estimate of the signal power spectrum of said frame, P.sub.N is an estimate of the noise power spectrum of said frame, and .kappa. is a constant.

### Claim 12

A speech system comprising:
a. a speech acquiring module;
b. a noise suppressing module couple to said acquiring module;
c. an analyzing module coupled to said suppressing module;
d. a transmitting/storing module coupled to said analyzing module;
e. a synthesizing module coupled to said transmitting/storing module;
f. a playingback module coupled to said synthesizing module;
g. wherein said noise suppressing module includes an attenuating filter with a noise suppressing factor depending upon E.sub.N divide by E.sub.Y where E.sub.N is an estimate of noise energy of a frame of speech and E.sub.Y is an estimate of signal energy of said frame.