001444458 000__ 03407cam\a2200565Ii\4500
001444458 001__ 1444458
001444458 003__ OCoLC
001444458 005__ 20230310003711.0
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001444458 019__ $$a1298383985$$a1298513426$$a1298559853
001444458 020__ $$a9783030934057$$q(electronic bk.)
001444458 020__ $$a3030934055$$q(electronic bk.)
001444458 020__ $$z9783030934040$$q(print)
001444458 020__ $$z3030934047
001444458 0247_ $$a10.1007/978-3-030-93405-7$$2doi
001444458 035__ $$aSP(OCoLC)1297829259
001444458 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ
001444458 049__ $$aISEA
001444458 050_4 $$aTK7882.S65
001444458 08204 $$a621.382/2$$223
001444458 1001_ $$aMourad, Talbi,$$eauthor.
001444458 24514 $$aThe stationary bionic wavelet transform and its applications for ECG and speech processing /$$cTalbi Mourad.
001444458 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001444458 300__ $$a1 online resource (xiv, 84 pages) :$$billustrations (some color).
001444458 336__ $$atext$$btxt$$2rdacontent
001444458 337__ $$acomputer$$bc$$2rdamedia
001444458 338__ $$aonline resource$$bcr$$2rdacarrier
001444458 4901_ $$aSignals and communication technology,$$x1860-4870
001444458 504__ $$aIncludes bibliographical references and index.
001444458 5050_ $$a1. Speech enhancement based on stationary bionic wavelet transform and maximum a posterior estimator of magnitude-squared spectrum -- 2. ECG denoising based on 1-D double-density complex DWT and SBWT -- 3. Speech Enhancement based on SBWT and MMSE Estimate of Spectral Amplitude -- 4. Arabic Speech Recognition by Stationary Bionic Wavelet Transform and MFCC using a Multi-Layer Perceptron for Voice Control.
001444458 506__ $$aAccess limited to authorized users.
001444458 520__ $$aThis book first details a proposed Stationary Bionic Wavelet Transform (SBWT) for use in speech processing. The author then details the proposed techniques based on SBWT. These techniques are relevant to speech enhancement, speech recognition, and ECG de-noising. The techniques are then evaluated by comparing them to a number of methods existing in literature. For evaluating the proposed techniques, results are applied to different speech and ECG signals and their performances are justified from the results obtained from using objective criterion such as SNR, SSNR, PSNR, PESQ , MAE, MSE and more. Describes and applies a proposed Stationary Bionic Wavelet Transform (SBWT) Discusses how speech enhancement, speech recognition, and ECG de-noising are aided by SBWTs Relevant to researchers, professionals, students, and academics in speech and ECG processing.
001444458 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 17, 2022).
001444458 650_0 $$aSpeech processing systems$$xMathematics.
001444458 650_0 $$aSignal processing$$xMathematics.
001444458 650_0 $$aWavelets (Mathematics)
001444458 650_6 $$aTraitement automatique de la parole$$xMathématiques.
001444458 650_6 $$aTraitement du signal$$xMathématiques.
001444458 650_6 $$aOndelettes.
001444458 655_0 $$aElectronic books.
001444458 77608 $$iPrint version: $$z3030934047$$z9783030934040$$w(OCoLC)1286146011
001444458 830_0 $$aSignals and communication technology,$$x1860-4870
001444458 852__ $$bebk
001444458 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-93405-7$$zOnline Access$$91397441.1
001444458 909CO $$ooai:library.usi.edu:1444458$$pGLOBAL_SET
001444458 980__ $$aBIB
001444458 980__ $$aEBOOK
001444458 982__ $$aEbook
001444458 983__ $$aOnline
001444458 994__ $$a92$$bISE