000 | 03242nam a22005655i 4500 | ||
---|---|---|---|
999 |
_c200458381 _d76593 |
||
003 | TR-AnTOB | ||
005 | 20231116200023.0 | ||
007 | cr nn 008mamaa | ||
008 | 220214s2022 sz | s |||| 0|eng d | ||
020 | _a9783030934057 | ||
024 | 7 |
_a10.1007/978-3-030-93405-7 _2doi |
|
040 |
_aTR-AnTOB _beng _cTR-AnTOB _erda |
||
041 | _aeng | ||
050 | 4 | _aTK7882.S65 | |
072 | 7 |
_aTJF _2bicssc |
|
072 | 7 |
_aUYS _2bicssc |
|
072 | 7 |
_aTEC008000 _2bisacsh |
|
072 | 7 |
_aTJF _2thema |
|
072 | 7 |
_aUYS _2thema |
|
090 | _aTK7882.S65EBK | ||
100 | 1 |
_aMourad, Talbi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 4 |
_aThe Stationary Bionic Wavelet Transform and its Applications for ECG and Speech Processing _h[electronic resource] / _cby Talbi Mourad. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
|
300 | _a1 online resource | ||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_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. | ||
650 | 0 | _aSignal processing. | |
650 | 0 | _aComputational intelligence. | |
650 | 0 | _aComputer vision. | |
650 | 1 | 4 | _aSignal, Speech and Image Processing . |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aComputer Vision. |
653 | 0 | _aSpeech processing systems -- Mathematics | |
653 | 0 | _aSignal processing -- Mathematics | |
653 | 0 | _aWavelets (Mathematics) | |
710 | 2 | _aSpringerLink (Online service) | |
830 | 0 |
_aSignals and Communication Technology, _x1860-4870 |
|
856 | 4 | 0 |
_uhttps://doi.org/10.1007/978-3-030-93405-7 _3Springer eBooks _zOnline access link to the resource |
942 |
_2lcc _cEBK |