Speech processing

Details Speech processing Speech processing

1997-07-09

1999-12-28

Sax, Steven

Data processing: speech signal processing, linguistics, language

Speech signal processing

For storage or transmission

704211, 704216, H03G 700

Patent

active

060093854

DESCRIPTION:

BRIEF SUMMARY
BACKGROUND OF THE INVENTION

1. Field of the Invention
The present invention is concerned with processing of speech signals, particularly those which have been distorted by amplitude-limiting processes such as clipping.
2. Related Art
Apart from its obvious effect on perceived speech quality, clipping in a telecommunications system is disadvantageous in that it reduces the dynamic range of the signal which can adversely affect the operation of echo cancellers. According to the present invention there is provided an apparatus for processing speech comprising: means to apply to a speech signal a wavelet transform to generate a plurality of transformed components each of which is the convolution of the signal and a respective one of a set of wavelets g(t/a.sub.i) where a.sub.i is a temporal scaling factor for that component and g(t) is a temporally finite waveform having a mean value of zero; means to modify the components; and means to apply to the modified components the inverse of the said wavelet transform, to produce an output signal; wherein the modifying means is operable to scale at least some of the components differently from one another such as to increase the dynamic range of the output signal.
Other, preferred, aspects of the invention are defined in the claims.


BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a block diagram of one form of speech processing apparatus according to the invention;
FIGS. 2 and 3 are a block diagram of two possible implementations of the wavelet transform unit of FIG. 1;
FIGS. 4 and 5 are block diagrams of two possible implementations of the inverse transform;
FIGS. 6a and 6b show graphically two versions of the Daubechies wavelet;
FIGS. 7a-7d provide a graph of a test speech waveform;
FIGS. 8a-8f and 9a-9f are graphs showing respectively the transformed version of the test waveform and the clipped test waveform;
FIG. 10 shows one implementation of the processing unit in FIG. 1;
FIGS. 11a and 11b are a graphical representation of a test waveform and a clipped test waveform after processing by the apparatus of FIG. 1; and
FIGS. 12a-14d show some alternative wavelets.


DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The apparatus of FIG. 1 is designed to receive, at an input 1, speech signals which have been distorted by clipping. The input signals are assumed to be in the form of digital samples at some sampling rate f.sub.s, e.g. 8 kHz. On the assumption that, because of the clipping, the signal employs the whole of the available dynamic range of the digital representation, it is firstly multiplied, in a multiplier 2, by a scaling factor S.sub.1 (S.sub.1 <1 ) to allow "headroom" for subsequent processing. Of course, an analogue-to-digital converter may be added if an analogue input is required. The signals are then supplied to a filter arrangement 3 which applies to the signals a Wavelet Transform, to produce N (e.g. five) outputs corresponding to respective transform levels. The series of signals V.sub.i (i=1, . . . N) appearing at these outputs are fed to a processing unit 4 which scales or otherwise processes them to produce N processed outputs V.sub.i ' which are then subject to the inverse wavelet transform in an inverse transform unit 5, to provide, after further scaling by a multiplier 6, a reconstructed speech signal at an output 7.
The general form of the wavelet transform W.sub.g of a function f(t) is ##EQU1## where g is the transform kernel.
If b is regarded as the independent variable of W.sub.g, and expressed as a time series for discrete values a.sub.i of a, then writing also a summation for the integral as we are dealing with a discrete system) we have a set of series for the transformed signal: ##EQU2## where i (i=1, . . . N) is the level of the series and n is the number of filter coefficients.
If we write g(x/a.sub.i)=g.sub.i (x) then ##EQU3## which can be implemented by a bank of N filters having coefficients given by

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Irino et al, "Signal Reconstruction application to Auditory Signal Processing", ICASSP 92: 1992 IEEE International Conference on Acoustics, Speech and Signal Processing (CAT. No. 92CH3103-9), ISBN 0-7803-0532-9, 1992, New York, NY, US XP002000224.

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