000			03402nam a22004335i 4500
999			_c387592 _d387592
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003			ES-MaUEC
005			20230327153708.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2009 sz | s |||| 0|eng d
020			_a9783031025587
024	7		_a10.1007/978-3-031-02558-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQC228.2 _b2009 EB
100			_aChristensen, Mads G. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9101053
245	1	0	_aMulti-Pitch Estimation _cby Mads Christensen, Andreas Jakobsson
250			_a1st edition 2009
264		1	_aCham _bSpringer International Publishing _c2009
300			_a1 recurso en línea (XVII, 141 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Speech and Audio Processing _x1932-1678
505	0		_aFundamentals -- Statistical Methods -- Filtering Methods -- Subspace Methods -- Amplitude Estimation.
520			_aPeriodic signals can be decomposed into sets of sinusoids having frequencies that are integer multiples of a fundamental frequency. The problem of finding such fundamental frequencies from noisy observations is important in many speech and audio applications, where it is commonly referred to as pitch estimation. These applications include analysis, compression, separation, enhancement, automatic transcription and many more. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented. The basic signal models and associated estimation theoretical bounds are introduced, and the properties of speech and audio signals are discussed and illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and maximum a posteriori methods, filtering methods based on both static and optimal adaptive designs, and subspace methods based on the principles of subspace orthogonality and shift-invariance. The application of these methods to analysis of speech and audio signals is demonstrated using both real and synthetic signals, and their performance is assessed under various conditions and their properties discussed. Finally, the estimators are compared in terms of computational and statistical efficiency, generalizability and robustness. Table of Contents: Fundamentals / Statistical Methods / Filtering Methods / Subspace Methods / Amplitude Estimation.
988			_aSynthesis Collection of Technology_2009
650		7	_2embne _9150608 _aProceso de señales _xMétodos estadísticos
650		7	_2embne _9143814 _aAudiometría _xModelos matemáticos
650		7	_2embne _9675808 _aVoz _xMedición _xProceso de datos
700	1		_aJakobsson, Andreas _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687745
776	0	8	_iPrinted edition: _z9783031014307
776	0	8	_iPrinted edition: _z9783031036866
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02558-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI