This document describes my diploma thesis titled:
Kalman Filtering and Speech Enhancement
Abstract
The enhancement of noisy speech is a challenging
research field with numerous applications. In the presented work we
focus on the case of speech signal corrupted by slowly varying,
non-white, additive noise, when only a corrupted signal is
available. First, the survey of speech enhancement, identification and
filtering techniques is presented. Second, a new speech enhancement
algorithm based on Kalman smoothing, spectral minima tracking,
state-space identification and all-pole modelling is proposed. The
intended application of this algorithm is the suppression of noise in
a running car environment for hands-free mobile telephony. Its
performance is compared to the traditional methods: it is found that
it can give better results at the expense of execution speed. Its
usability in the speech recognition setting and the effect of changes
to various parameters is demonstrated. A conventional as well as
a parallel version (using Parallel Virtual Machine) of the algorithm
discussed were developped. Sources are available in C and Matlab.
Keywords: speech enhancement, noise reduction, Kalman
filtering, smoothing, spectral subtraction, system identification,
parameter estimation, all-pole modelling, state-space identification,
total least squares, structured total least squares,
Hankel total least squares, singular value decomposition,
spectral minima tracking, speech recognition, hands-free mobile
telephony, Matlab, C, PVM, parallel computing.
AMS classification: 62M10, 62M20, 60G35, 68Q22, 93B30.
Download
The report is available as
compressed Postscript, in a reduced
form (two pages on one A4 sheet), or as a pdf. Here are the
slides I use for presentation and
the archive containing all the programs and scripts
I have written.
Links
Have a look at our
research unit, my
bookmark page
and home page.
Demonstration
Listen to the original signal, the signal processed by
Reiner Martin's algorithm and the same signal
processed by our so far best Kalman filtering
algorithm for different parameter settings - version
1,
2,
3 and
4 (our best so far - using MEM spectrum estimate).
Somewhat longer samples:
original, signal processed by
spectral subtraction,
Doblinger's algorithm,
Martin's algorithm,
and the new kalmse algorithm,
Mail me if you are interested.
Jan Kybic, kybic@ieee.org
[My home page]