000			06124nam a2200529 i 4500
001			6812518
003			IEEE
005			20200413152857.0
006			m eo d
007			cr cn |||m|||a
008			100306s2010 caua foab 000 0 eng d
020			_a9781608451074 (electronic bk.)
020			_z9781608451067 (pbk.)
024	7		_a10.2200/S00258ED1V01Y201003ASE006 _2doi
035			_a(CaBNVSL)gtp00538724
035			_a(OCoLC)587603812
040			_aCaBNVSL _cCaBNVSL _dCaBNVSL
050		4	_aTK5102.9 _b.K652 2010
082	0	4	_a621.3822 _222
100	1		_aKokkinakis, Kostas.
245	1	0	_aAdvances in modern blind signal separation algorithms _h[electronic resource] : _btheory and applications / _cKostas Kokkinakis and Philipos C. Loizou.
260			_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool Publishers, _cc2010.
300			_a1 electronic text (viii, 88 p. : ill.) : _bdigital file.
490	1		_aSynthesis lectures on algorithms and software in engineering, _x1938-1735 ; _v# 6
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
504			_aIncludes bibliographical references (p. 75-86).
505	0		_a1. Fundamentals of blind signal separation -- Introduction -- Applications -- Instantaneous mixing model -- Assumptions -- Independent & principal component analysis -- Illustration of PCA and ICA -- Acoustics -- Convolutive mixing model -- Summary --
505	8		_a2. Modern blind signal separation algorithms -- Introduction -- Information-theoretic criteria -- Kullback-Leibler divergence -- Entropy maximization -- Adaptive algorithms for BSS -- Stochastic gradient -- Natural (or relative) gradient -- Parametric source density models for BSS -- Generalized Gaussian density -- Moment matching estimators -- Parametric score functions -- Blind signal separation strategies for convolutive mixtures -- Convolutive BSS based on second-order statistics -- Convolutive BSS based on higher-order statistics -- Scaling and permutation -- Performance measures -- Summary --
505	8		_a3. Application of blind signal processing strategies to noise reduction for the hearing-impaired -- Introduction -- Hearing loss -- Hearing aids -- Cochlear implants -- Speech intelligibility in noise -- Noise reduction strategies for hearing-impaired listeners -- 2M-SESS strategy -- 2M2-BEAM strategy -- 4M-SESS strategy -- Speech intelligibility studies with hearing-impaired listeners -- Subjects -- Procedure -- Stimuli -- Anechoic conditions -- Reverberant conditions -- Results & Discussion -- Anechoic conditions -- Effects of training on speech intelligibility -- Reverberant conditions --
505	8		_a4. Conclusions and future challenges -- Bibliography -- Authors' biographies.
506	1		_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510	0		_aCompendex
510	0		_aINSPEC
510	0		_aGoogle scholar
510	0		_aGoogle book search
520	3		_aWith human-computer interactions and hands-free communications becoming overwhelmingly important in the new millennium, recent research efforts have been increasingly focusing on state-of-the-art multi-microphone signal processing solutions to improve speech intelligibility in adverse environments. One such prominent statistical signal processing technique is blind signal separation (BSS). BSS was first introduced in the early 1990s and quickly emerged as an area of intense research activity showing huge potential in numerous applications. BSS comprises the task of 'blindly' recovering a set of unknown signals, the so-called sources from their observed mixtures, based on very little to almost no prior knowledge about the source characteristics or the mixing structure. The goal of BSS is to process multi-sensory observations of an inaccessible set of signals in a manner that reveals their individual (and original) form, by exploiting the spatial and temporal diversity, readily accessible through a multi-microphone configuration. Proceeding blindly exhibits a number of advantages, since assumptions about the room configuration and the source-to-sensor geometry can be relaxed without affecting overall efficiency. This booklet investigates one of the most commercially attractive applications of BSS, which is the simultaneous recovery of signals inside a reverberant (naturally echoing) environment, using two (or more) microphones. In this paradigm, each microphone captures not only the direct contributions from each source, but also several reflected copies of the original signals at different propagation delays. These recordings are referred to as the convolutive mixtures of the original sources. The goal of this booklet in the lecture series is to provide insight on recent advances in algorithms, which are ideally suited for blind signal separation of convolutive speech mixtures. More importantly, specific emphasis is given in practical applications of the developed BSS algorithms associated with real-life scenarios. The developed algorithms are put in the context of modern DSP devices, such as hearing aids and cochlear implants, where design requirements dictate low power consumption and call for portability and compact size. Along these lines, this booklet focuses on modern BSS algorithms which address (1) the limited amount of processing power and (2) the small number of microphones available to the end-user.
530			_aAlso available in print.
588			_aTitle from PDF t.p. (viewed on March 6, 2010).
650		0	_aBlind source separation _xMathematical models.
700	1		_aLoizou, Philipos C.
730	0		_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on algorithms and software in engineering, _x1938-1735 ; _v# 6.
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812518
999			_c561739 _d561739