A spectral subtraction method for the enhancement of speech corrupted by nonwhite, nonstationary noise

Abstract

Spectral subtraction is a popular method for the enhancement of the quality of speech corrupted by additive noise. Implementations of spectral subtraction require an available estimate of the corrupting noise. The spectrum of the noise is usually estimated during a period of time known a priori to be speech free. This estimate is then assumed to remain stationary over the entire noisy speech signal. This paper makes use of a standard spectral subtraction algorithm. However, the method does not require a noise estimation obtained from a period of time when speech is known not to exist. Instead, use is made of a continuously running noise estimation algorithm to track the noise which is input to the spectral subtraction process. As a result the method is novel in that it: (1) does not require a known nonspeech interval from which to determine the noise, and (2) can handle both nonwhite and slowly varying (relative to the speech) noise in an automatic way. Speech features which are used to estimate the noise content during speech are the voiced/unvoiced decision, pitch frequency estimate and the confidence of these features. Results show that the quality of speech degraded by nonwhite, nonstationary noise can be improved using spectral subtraction with the proposed noise estimation algorithm.