000			04438nam a22004815i 4500
001			978-1-4020-6479-1
003			DE-He213
005			20251006084534.0
007			cr nn 008mamaa
008			100301s2007 ne | s |||| 0|eng d
020			_a9781402064791
020			_a99781402064791
024	7		_a10.1007/978-1-4020-6479-1 _2doi
082	0	4	_a621.382 _223
100	1		_aMakino, Shoji. _eeditor.
245	1	0	_aBlind Speech Separation _h[electronic resource] / _cedited by Shoji Makino, Hiroshi Sawada, Te-Won Lee.
264		1	_aDordrecht : _bSpringer Netherlands, _c2007.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSignals and Communication Technology, _x1860-4862
505	0		_aMultiple 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.
520			_aThis 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.
650		0	_aENGINEERING.
650		0	_aMICROWAVES.
650		0	_aTELECOMMUNICATION.
650	1	4	_aENGINEERING.
650	2	4	_aSIGNAL, IMAGE AND SPEECH PROCESSING.
650	2	4	_aCOMMUNICATIONS ENGINEERING, NETWORKS.
650	2	4	_aMICROWAVES, RF AND OPTICAL ENGINEERING.
700	1		_aSawada, Hiroshi. _eeditor.
700	1		_aLee, Te-Won. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781402064784
830		0	_aSignals and Communication Technology, _x1860-4862
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4020-6479-1 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-ENG
942			_2ddc _cER
999			_c61735 _d61735