Blind Speech Separation
Makino, Shoji.
Blind Speech Separation [electronic resource] / edited by Shoji Makino, Hiroshi Sawada, Te-Won Lee. - XVI, 432 p. online resource. - Signals and Communication Technology, 1860-4862 . - Signals and Communication Technology, .
Multiple Microphone Blind Speech Separation with ICA -- Convolutive Blind Source Separation for Audio Signals -- Frequency-Domain Blind Source Separation -- Blind Source Separation using Space–Time Independent Component Analysis -- TRINICON-based Blind System Identification with Application to Multiple-Source Localization and Separation -- SIMO-Model-Based Blind Source Separation – Principle and its Applications -- Independent Vector Analysis for Convolutive Blind Speech Separation -- Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation -- Underdetermined Blind Speech Separation with Sparseness -- The DUET Blind Source Separation Algorithm -- K-means Based Underdetermined Blind Speech Separation -- Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and L1-Norm Minimization -- Bayesian Audio Source Separation -- Single Microphone Blind Speech Separation -- Monaural Source Separation -- Probabilistic Decompositions of Spectra for Sound Separation -- Sparsification for Monaural Source Separation -- Monaural Speech Separation by Support Vector Machines: Bridging the Divide Between Supervised and Unsupervised Learning Methods.
This is the first book to provide a cutting edge reference to the fascinating topic of blind source separation (BSS) for convolved speech mixtures. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications. The individual chapters are designed to be tutorial in nature with specific emphasis on an in-depth treatment of state of the art techniques. Blind Speech Separation is divided into three parts: Part 1 presents overdetermined or critically determined BSS. Here the main technology is independent component analysis (ICA). ICA is a statistical method for extracting mutually independent sources from their mixtures. This approach utilizes spatial diversity to discriminate between desired and undesired components, i.e., it reduces the undesired components by forming a spatial null towards them. It is, in fact, a blind adaptive beamformer realized by unsupervised adaptive filtering. Part 2 addresses underdetermined BSS, where there are fewer microphones than source signals. Here, the sparseness of speech sources is very useful; we can utilize time-frequency diversity, where sources are active in different regions of the time-frequency plane. Part 3 presents monaural BSS where there is only one microphone. Here, we can separate a mixture by using the harmonicity and temporal structure of the sources. We can build a probabilistic framework by assuming a source model, and separate a mixture by maximizing the a posteriori probability of the sources.
9781402064791
10.1007/978-1-4020-6479-1 doi
Engineering.
Microwaves.
Telecommunication.
Engineering.
Signal, Image and Speech Processing.
Communications Engineering, Networks.
Microwaves, RF and Optical Engineering.
TK5102.9 TA1637-1638 TK7882.S65
621.382
Blind Speech Separation [electronic resource] / edited by Shoji Makino, Hiroshi Sawada, Te-Won Lee. - XVI, 432 p. online resource. - Signals and Communication Technology, 1860-4862 . - Signals and Communication Technology, .
Multiple Microphone Blind Speech Separation with ICA -- Convolutive Blind Source Separation for Audio Signals -- Frequency-Domain Blind Source Separation -- Blind Source Separation using Space–Time Independent Component Analysis -- TRINICON-based Blind System Identification with Application to Multiple-Source Localization and Separation -- SIMO-Model-Based Blind Source Separation – Principle and its Applications -- Independent Vector Analysis for Convolutive Blind Speech Separation -- Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation -- Underdetermined Blind Speech Separation with Sparseness -- The DUET Blind Source Separation Algorithm -- K-means Based Underdetermined Blind Speech Separation -- Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and L1-Norm Minimization -- Bayesian Audio Source Separation -- Single Microphone Blind Speech Separation -- Monaural Source Separation -- Probabilistic Decompositions of Spectra for Sound Separation -- Sparsification for Monaural Source Separation -- Monaural Speech Separation by Support Vector Machines: Bridging the Divide Between Supervised and Unsupervised Learning Methods.
This is the first book to provide a cutting edge reference to the fascinating topic of blind source separation (BSS) for convolved speech mixtures. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications. The individual chapters are designed to be tutorial in nature with specific emphasis on an in-depth treatment of state of the art techniques. Blind Speech Separation is divided into three parts: Part 1 presents overdetermined or critically determined BSS. Here the main technology is independent component analysis (ICA). ICA is a statistical method for extracting mutually independent sources from their mixtures. This approach utilizes spatial diversity to discriminate between desired and undesired components, i.e., it reduces the undesired components by forming a spatial null towards them. It is, in fact, a blind adaptive beamformer realized by unsupervised adaptive filtering. Part 2 addresses underdetermined BSS, where there are fewer microphones than source signals. Here, the sparseness of speech sources is very useful; we can utilize time-frequency diversity, where sources are active in different regions of the time-frequency plane. Part 3 presents monaural BSS where there is only one microphone. Here, we can separate a mixture by using the harmonicity and temporal structure of the sources. We can build a probabilistic framework by assuming a source model, and separate a mixture by maximizing the a posteriori probability of the sources.
9781402064791
10.1007/978-1-4020-6479-1 doi
Engineering.
Microwaves.
Telecommunication.
Engineering.
Signal, Image and Speech Processing.
Communications Engineering, Networks.
Microwaves, RF and Optical Engineering.
TK5102.9 TA1637-1638 TK7882.S65
621.382