Of the two primary approaches to the classic source separation
problem, only one does not impose potentially unreasonable model
and likelihood constraints: the Bayesian statistical approach.
Bayesian methods incorporate the available information regarding
the model parameters and not only allow estimation of the sources
and mixing coefficients, but also allow inferences to be drawn from
them. Multivariate Bayesian Statistics: Models for Source
Separation and Signal Unmixing offers a thorough, self-contained
treatment of the source separation problem. After an introduction
to the problem using the "cocktail-party" analogy, Part I provides
the statistical background needed for the Bayesian source
separation model. Part II considers the instantaneous constant
mixing models, where the observed vectors and unobserved sources
are independent over time but allowed to be dependent within each
vector. Part III details more general models in which sources can
be delayed, mixing coefficients can change over time, and
observation and source vectors can be correlated over time. For
each model discussed, the author gives two distinct ways to
estimate the parameters. Real-world source separation problems,
encountered in disciplines from engineering and computer science to
economics and image processing, are more difficult than they
appear. This book furnishes the fundamental statistical material
and up-to-date research results that enable readers to understand
and apply Bayesian methods to help solve the many "cocktail party"
problems they may confront in practice.
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