000 03555nam a22005175i 4500
001 978-3-031-02558-7
003 DE-He213
005 20240730164002.0
007 cr nn 008mamaa
008 220601s2009 sz | s |||| 0|eng d
020 _a9783031025587
_9978-3-031-02558-7
024 7 _a10.1007/978-3-031-02558-7
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
_2thema
082 0 4 _a621.3
_223
100 1 _aChristensen, Mads.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981506
245 1 0 _aMulti-Pitch Estimation
_h[electronic resource] /
_cby Mads Christensen, Andreas Jakobsson.
250 _a1st ed. 2009.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2009.
300 _aXVII, 141 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aElectrical engineering.
_981507
650 0 _aSignal processing.
_94052
650 0 _aAcoustical engineering.
_99499
650 1 4 _aElectrical and Electronic Engineering.
_981508
650 2 4 _aSignal, Speech and Image Processing.
_931566
650 2 4 _aEngineering Acoustics.
_931982
700 1 _aJakobsson, Andreas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981509
710 2 _aSpringerLink (Online service)
_981510
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031014307
776 0 8 _iPrinted edition:
_z9783031036866
830 0 _aSynthesis Lectures on Speech and Audio Processing,
_x1932-1678
_981511
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02558-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c85188
_d85188