Copertina di Novel Approach for Single Channel Blind Source Separation
Titolo del libro:

Novel Approach for Single Channel Blind Source Separation

Unsupervised Learning Algorithms and Applications

LAP LAMBERT Academic Publishing (02.10.2012 )

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Risvolto di copertina:
Single channel blind source separation (SCBSS) is an intensively researched field with numerous important applications. This book proposes a novel method based on variable regularised sparse nonnegative matrix factorization which decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral basis and temporal code of the sources. To further improve the previous work, a new method is developed based on decomposing the mixture into a series of oscillatory components termed as intrinsic mode functions (IMF). It is shown that IMFs have several desirable properties unique to SCBSS and how these properties can be advantaged to relax the constraints posed by the problem. In addition, this book develops a novel method for feature extraction using psycho-acoustic model and a family of Itakura-Saito divergence based novel matrix factorization has been developed. The proposed matrix factorizations have the property of scale invariant which enables lower energy components to be treated with equal importance as the high energy ones. Results show that all the developed algorithms presented in this book outperformed conventional methods
Casa editrice:
LAP LAMBERT Academic Publishing
Sito Web:
Da (autore):
Bin Gao, Wai Lok Woo
Numero di pagine:
Pubblicato il:
Giacenza di magazzino:
Elettronica, Elettrotecnica, tecnologia dell'informazione e della comunicazione
68,00 €
Parole chiave:
source separation;non-negative matrix factorization;audio processing learning;sparsity-aware learning;Time-frequency representation

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