Spectral subtraction noise suppression method

• US 5,943,429 A
• Filed: 07/28/1997
• Issued: 08/24/1999
• Est. Priority Date: 01/30/1995
• Status: Expired due to Term

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: Handel, Peter
• Primary Examiner(s): Loomis, Paul
• Assistant Examiner(s): Mei, Xu
• Application Number:
  US08/875,412
• Time in Patent Office: 757 Days
• Field of Search: 381/71.1, 381/71.9-71.14, 381/94.1-94.4, 381/94.7-94.9, 381/FOR 123, 381/124, 381/115, 381/116, 381/317, 381/318, 704/226-228
• US Class Current: 381/94.2
• CPC Class Codes: G10L 2021/02168 the estimation exclusively taking place during speech pauses
• CPC Class Codes: G10L 21/02 Speech enhancement, e.g. noise reduction or echo cancellation reducing echo effects in line transmission systems H04B3/20; echo suppression in hands-free telephones H04M9/08
• CPC Class Codes: G10L 21/0264 characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques

Abstract

A spectral subtraction noise suppression method in a frame based digital communication system is described. Each frame includes a predetermined number N of audio samples, thereby giving each frame N degrees of freedom. The method is performed by a spectral subtraction function H(w) which is based on an estimate of the power spectral density of background noise of non-speech frames and an estimate Φ_x (w) of the power spectral density of speech frames. Each speech frame is approximated by a parametric model that reduces the number of degrees of freedom to less than N. The estimate Φ_x (w) of the power spectral density of each speech frame is estimated from the approximative parametric model.

Claims

1. A spectral subtraction noise suppression method in a frame based digital communication system, each frame including a predetermined number N of audio samples, thereby giving each frame N degrees of freedom, wherein a spectral subtraction function H(ω

) is based on an estimate Φ

_v (ω

) of a power spectral density of background noise of non-speech frames and an estimate Φ

_x (ω

) of a power spectral density of speech frames comprising the steps of;

approximating each speech frame by a parametric model that reduces the number of degrees of freedom to less than N;

estimating said estimate Φ

_x (ω

) of the power spectral density of each speech frame by a parametric power spectrum estimation method based on the approximative parametric model; and

estimating said estimate Φ

_v (ω

) of the power spectral density of each non-speech frame by a non-parametric power spectrum estimation method.

2. The method of claim 1, wherein the approximative parametric model is an autoregressive (AR) model.

3. The method of claim 2, wherein the autoregressive (AR) model is approximately of order √

N.

4. The method of claim 3, wherein the autoregressive (AR) model is approximately of order 10.

5. The method of claim 3, wherein the a spectral subtraction function H(ω

) is in accordance with the formula;

##EQU45## where G(ω

) is a weighting function and δ

(ω

) is a subtraction factor.

6. The method of claim 5, wherein G(ω

)=1.

7. The method of claim 5, wherein δ

(ω

) is a constant ≦

1.

8. The method of claim 3, wherein the a spectral subtraction function H(ω

) is in accordance with the formula;

##EQU46##

9. The method of claim 3, wherein the a spectral subtraction function H(ω

) is in accordance with the formula;

10. The method of claim 3, wherein the spectral subtraction function H(ω

) is in accordance with the formula;