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008 220601s2011 sz | s |||| 0|eng d
020 _a9783031025617
_9978-3-031-02561-7
024 7 _a10.1007/978-3-031-02561-7
_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 _aBenesty, Jacob.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985883
245 1 2 _aA Perspective on Single-Channel Frequency-Domain Speech Enhancement
_h[electronic resource] /
_cby Jacob Benesty, Yiteng Huang.
250 _a1st ed. 2011.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2011.
300 _aVIII, 101 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 _aIntroduction -- Problem Formulation -- Performance Measures -- Linear and Widely Linear Models -- Optimal Filters with Model 1 -- Optimal Filters with Model 2 -- Optimal Filters with Model 3 -- Optimal Filters with Model 4 -- Experimental Study.
520 _aThis book focuses on a class of single-channel noise reduction methods that are performed in the frequency domain via the short-time Fourier transform (STFT). The simplicity and relative effectiveness of this class of approaches make them the dominant choice in practical systems. Even though many popular algorithms have been proposed through more than four decades of continuous research, there are a number of critical areas where our understanding and capabilities still remain quite rudimentary, especially with respect to the relationship between noise reduction and speech distortion. All existing frequency-domain algorithms, no matter how they are developed, have one feature in common: the solution is eventually expressed as a gain function applied to the STFT of the noisy signal only in the current frame. As a result, the narrowband signal-to-noise ratio (SNR) cannot be improved, and any gains achieved in noise reduction on the fullband basis come with a price to pay, which is speechdistortion. In this book, we present a new perspective on the problem by exploiting the difference between speech and typical noise in circularity and interframe self-correlation, which were ignored in the past. By gathering the STFT of the microphone signal of the current frame, its complex conjugate, and the STFTs in the previous frames, we construct several new, multiple-observation signal models similar to a microphone array system: there are multiple noisy speech observations, and their speech components are correlated but not completely coherent while their noise components are presumably uncorrelated. Therefore, the multichannel Wiener filter and the minimum variance distortionless response (MVDR) filter that were usually associated with microphone arrays will be developed for single-channel noise reduction in this book. This might instigate a paradigm shift geared toward speech distortionless noise reduction techniques. Table of Contents: Introduction / Problem Formulation / Performance Measures / Linear and Widely Linear Models / Optimal Filters with Model 1 / Optimal Filters with Model 2 / Optimal Filters with Model 3 / Optimal Filters with Model 4 / Experimental Study.
650 0 _aElectrical engineering.
_985884
650 0 _aSignal processing.
_94052
650 0 _aAcoustical engineering.
_99499
650 1 4 _aElectrical and Electronic Engineering.
_985886
650 2 4 _aSignal, Speech and Image Processing.
_931566
650 2 4 _aEngineering Acoustics.
_931982
700 1 _aHuang, Yiteng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985889
710 2 _aSpringerLink (Online service)
_985891
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031014338
776 0 8 _iPrinted edition:
_z9783031036897
830 0 _aSynthesis Lectures on Speech and Audio Processing,
_x1932-1678
_985893
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02561-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c85873
_d85873