3 research outputs found
Blind source separation of audio streams using wavelets
In this thesis, Blind Source Separation is studied in the context of both instantaneous and
convolutive mixtures of musical sources. In order to appreciate how the application of
BSS techniques to music may differ from application to speech samples, the statistical and
spectral characteristics of commercial musical recordings are determined. Furthermore,
the extent to which certain assumptions underlying many BSS (or, more generally, ICA)
techniques are valid is investigated.
The use of short-time Fourier Transform techniques in BSS is discussed, and the use of
wavelet and wavelet-packet techniques to perform BSS is investigated. The most efficient
wavelets to use for the decomposition of musical sources are determined, and measures of
the sparsity of transformed sample data are compared for different transform techniques.
A number of strategies for reducing the computational complexity of the application of
BSS techniques are developed (principally around the use of wavelet transforms).
Summaries of the most popular BSS techniques (for both instantaneous and convolutive
mixtures) are provided, and metrics frequently used to evaluate both signal separation
and reconstruction performance are listed.
To support the work of this thesis, a large library of Matlab routines has been developed.
Throughout the work, clear examples are provided to support the theoretical discussion.
This thesis concludes with a summary of the investigations performed, and an outline of
future research directions suggested by the work contained herein
Statistical, spectral and stochastic characteristics of music
The goal of this paper is to explore some of the principal
spectral and statistical characteristics of music that are relevant
to the problem of Blind Source Separation (BSS). Some
BSS algorithms require some a priori knowledge of statistical
characteristics of the mixtures under examination, if
only to more accurately establish initial estimates. Furthermore,
theoretical investigations depend upon the assumption
of Wide-Sense Stationarity - the extent to which this assumption
holds is investigated
Wavelet-based transforms as a means of signal separation
The goal of this paper is to facilitate the use of waveletbased
transforms in BSS (and, more generally, ICA) algorithms.
We investigate the performance of transforms such as
the ContinuousWavelet Transform, Discrete Wavelet Transform
and Discrete Wavelet Packet Transform relative to the
commonly-used Short-Time Fourier Transform in terms of
the efficiency of signal-power representation and the minimisation
of cross-channel effects in two-channel mixtures