Adaptive
Blind Noise Suppression in some Speech Processing Applications
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.
INTRODUCTION
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.
The classical systems for noise
suppression rely on the usage of adaptive linear filtering and the application
of digital filters with finite impulse response (FIR). The strong points of this
approach are the simple analysis of the linear systems in the process of adaptation
and the guaranteed stability of FIR structures. It is worth mentioning the existence
of relatively simple and well investigated adaptive algorithms for such kind of
systems as least mean squares (LMS) and recursive least squares (RLS) algorithms.
The investigations in the area of noise cancellation reveal that in some applications
the nonlinear filters outperform their linear counterparts. That fact is a good
motivation for a shift towards the usage of nonlinear systems in noise reduction
.Another approach is based on a microphone array instead of the two microphones,
reference and primary, that are used in the classical noise cancellation scheme
.
A brief analysis of all mentioned approaches
leads to the conclusion that they try to model the noise path either by a linear
or by a nonlinear system. Each of these methods has its strengths and weaknesses.
For example, for the classical noise cancellation with two microphones this is
the need of reference signal; for the neural filters - the fact that as a rule
they are slower than classic adaptive filters and they are efficient only for
noise suppression on relatively short data sequences which is not true for speech
processing and finally for microphone arrays - the need of precise space alignment
In present contribution the approach is slightly different.
The
basic idea is that in many applications, for instance, hands-free cellular phones
in car environment howling control in hands-free phones, noise reduction in an
office environment, the noise reveals specific features that can be exploited.
In most instances although the noise might be quite wide-band, there are always,
as a rule, no more than two or three regions of its frequency spectrum that carry
most of the noise energy and the removal of these dominant frequencies results
in a considerable improvement of S/N ratio. This brings the idea to use notch
adaptive filters capable of tracking the noise characteristics. In this paper
a modification of all-pass structures is used They are recursive, and at the same
time, are stable during the adaptive process. The approach is called "blind"
because there is no need of a reference signal.
