Abstract
The speech signal in general is corrupted by noise and the noise signal does not affect the speech signal uniformly over the entire spectrum. An improved Wiener filtering method is proposed in this paper for reducing background noise from speech signal in colored noise environments. In view of nonlinear variation of human ear sensibility in frequency spectrum, nonlinear multi-band Bark scale frequency spacing approach is used. The cross-correlation between the speech and noise signal is considered in the proposed method to reduce colored noise. To overcome harmonic distortion introduced in enhanced speech, in the proposed method regenerate the suppressed harmonics are regenerated. Objective and subjective tests were carried out to demonstrate improvement in the perceptual quality of speeches by the proposed technique.
Highlights
In many speech communication systems, recognition of speech signal from a corrupted speech signal with background noise is a challenging task especially at low SNR values
Speech quality and intelligibility might significantly deteriorate in the presence of background noise, especially when the speech signal is subjected to In many speech communication systems, background noise in corrupted speech is a challenging task especially at low SNR values
Speech quality and intelligibility might significantly deteriorate in the presence of background noise, especially when the speech signal is subject to subsequent processing, such as automatic speech recognition and speech coding
Summary
In many speech communication systems, recognition of speech signal from a corrupted speech signal with background noise is a challenging task especially at low SNR (signal to noise ratio) values. The power spectral subtraction and the Wiener filtering algorithms are widely used because of their low computational complexity and impressive performance In these algorithms the enhanced speech spectrum is obtained by subtracting an estimated noise spectrum from noisy speech spectrum or by multiplying the noisy spectrum with a gain function. A method, called regeneration of suppressed harmonics that takes into account the harmonic characteristic of speech, is proposed In this approach, the output signal of classic noise reduction technique is further processed to create an artificial signal where in the missing harmonics are automatically regenerated. The output signal of classic noise reduction technique is further processed to create an artificial signal where in the missing harmonics are automatically regenerated This artificial signal is used to refine the apriori SNR used to compute a spectral gain
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