000			03083nam a22004455i 4500
999			_c368605 _d368605
001			368605
003			ES-MaUEC
005			20230102121751.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220214s2022 sz |a s |||| 0|eng d
020			_a9783030934057
024	7		_a10.1007/978-3-030-93405-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.S65 _b2022 EB
100	1		_aMourad, Talbi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683864
245	1	4	_aThe Stationary Bionic Wavelet Transform and its Applications for ECG and Speech Processing _cby Talbi Mourad
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIV, 84 páginas) _b69 ilustraciones, 50 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSignals and Communication Technology _x1860-4870
505	0		_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.
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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9161271 _aWavelets (Matemáticas)
650		7	_2embne _9150608 _aProceso de señales
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
776	0	8	_iPrinted edition: _z9783030934040
776	0	8	_iPrinted edition: _z9783030934064
776	0	8	_iPrinted edition: _z9783030934071
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-93405-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b05/2022 _dz _esc _zSI