Date of Award

Spring 1977

Document Type

Thesis - Restricted

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering

First Advisor

Heinen, James A.

Second Advisor

Riedel, Susan A.

Third Advisor

Jaskolski, Stanley V.

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. The approach pursued in this thesis makes use of a standard spectral subtraction algorithm. However, the method does not require a noise estimate 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 in the signal which is input to the spectral subtraction process. As a result, the method is novel in that it (1) does not require a known non-speech interval from which to determine the noise, and (2) can handle both non-white 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 non-white, non-stationary noise can be improved using spectral subtraction with the proposed noise estimation algorithm.

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