ADAPTIVE BLIND NOISE SUPPRESSION IN SOME SPEECH PROCESSING APPLICATIONS

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In many applications of speech processing the noise reveals some specific features. Although the noise could be quite broadband, there are a limited number of dominant frequencies, which carry the most of its energy. This fact implies the usage of narrow-band notch filters that must be adaptive in order to track the changes in noise characteristics. In present contribution, a method and a system for noise suppression are developed. The method uses adaptive notch filters based on second-order Gray-Markel lattice structure. The main advantages of the proposed system are that it has very low computational complexity, is stable in the process of adaptation, and has a short time of adaptation. Under comparable SNR improvement, the proposed method adjusts only 3 coefficients against 250-450 for the conventional adaptive noise cancellation systems. A framework for a speech recognition system that uses the proposed method is suggested.

The noise existence is inevitable in real applications of speech processing. It is well known that the additive noise affects negatively the performance of the speech codecs designed to work with noise-free speech especially codecs based on linear prediction coefficients (LPC). Another application strongly influenced by noise is related to the hands free phones where the background noise reduces the signal to noise ratio (S/N) and the speech intelligibility.

Last but not least, is the problem of speech recognition in a noisy environment. A system that works well in noise-free conditions, usually shows considerable degradation in performance when background noise is present It is clear that a strong demand for reliable noise cancellation methods exists that efficiently separate the noise from speech signal. The endeavors in designing of such systems can be followed some 20 years ago The core of the problem is that in most situations the characteristics of the noise are not known a priori and moreover they may change in time. This implies the use of adaptive systems capable of identifying and tracking the noise characteristics. This is why the application of adaptive filtering for noise cancellation is widely used.

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