The speech is usually subject to noise and distortion, which result in the loss
of intelligibility of speech message. Therefore, the enhancement of speech
corrupted by noise is an important problem with numerous applications such as
suppression of environmental noise for communication systems and hearing aids, enhancing
the quality of old records.
The purpose of speech enhancement is to improve some perceptual aspects
of speech for the human listener or to improve the speech signal so that it may be
better exploited by other speech processing algorithms.
Speech enhancement depends on signal processing and human perceptual
factors. Since speech quality and intelligibility are dependent on short term
spectrum amplitude and insensitive to spectrum phase, the speech is always
considered stationary over a short period of time (10 ms to 20 ms) and it is processed
frame by frame. Some widely used processing methods are spectrum subtraction,
Wiener filtering and Iterative Wiener filtering.
The task involved an implementation of an enhancement algorithm using
spectrum subtraction to get an estimate of the uncorrupted speech while achieving the
highest possible intelligibility. Since it is hard to describe mathematically, we instead
use the mean squared value of the estimated error e as a measurement of this quality
as well as the SNR improvement (Berouti et
al., 1979).
The properties of the noise as well as the nature of corruption vary in
various scenarios, so we make the following assumptions in our implementation.
The background noise is slowly varying, additive and independent with the speech. It
is relatively long-time stationary compared with the speech so that its spectrum
magnitude expected values during the speech activity remain almost the same as prior to the
speech activity. |
