001444730 000__ 02886cam\a2200493Ia\4500
001444730 001__ 1444730
001444730 003__ OCoLC
001444730 005__ 20230310003725.0
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001444730 008__ 220302s2022\\\\sz\\\\\\ob\\\\001\0\eng\d
001444730 019__ $$a1301486837$$a1301766581$$a1301900175$$a1301946612$$a1302001190$$a1302012405
001444730 020__ $$a9783030914790$$q(electronic bk.)
001444730 020__ $$a3030914798$$q(electronic bk.)
001444730 020__ $$z303091478X
001444730 020__ $$z9783030914783
001444730 0247_ $$a10.1007/978-3-030-91479-0$$2doi
001444730 035__ $$aSP(OCoLC)1301449588
001444730 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dN$T$$dUKAHL$$dOCLCQ
001444730 049__ $$aISEA
001444730 050_4 $$aQ325.73
001444730 08204 $$a006.3/1$$223
001444730 1001_ $$aMittag, Gabriel.
001444730 24510 $$aDeep learning based speech quality prediction/$$cGabriel Mittag.
001444730 260__ $$aCham, Switzerland :$$bSpringer,$$c2022.
001444730 300__ $$a1 online resource
001444730 4901_ $$aT-labs series in telecommunication services
001444730 504__ $$aIncludes bibliographical references and index.
001444730 5050_ $$a1. Introduction -- 2. Quality Assessment of Transmitted Speech -- 3. Neural Network Architectures for Speech Quality Prediction -- 4. Double-Ended Speech Quality Prediction Using Siamese Networks -- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning -- 6. Bias-Aware Loss for Training From Multiple Datasets -- 7. NISQA A Single-Ended Speech Quality Model -- 8. Conclusions -- A. Dataset Condition Tables -- B. Train and Validation Dataset Dimension Histograms -- References.
001444730 506__ $$aAccess limited to authorized users.
001444730 520__ $$aThis book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness.
001444730 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 10, 2022).
001444730 650_0 $$aDeep learning (Machine learning)
001444730 650_0 $$aSpeech processing systems.
001444730 650_6 $$aTraitement automatique de la parole.
001444730 655_0 $$aElectronic books.
001444730 77608 $$iPrint version:$$z303091478X$$z9783030914783$$w(OCoLC)1280197573
001444730 830_0 $$aT-labs series in telecommunication services.
001444730 852__ $$bebk
001444730 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-91479-0$$zOnline Access$$91397441.1
001444730 909CO $$ooai:library.usi.edu:1444730$$pGLOBAL_SET
001444730 980__ $$aBIB
001444730 980__ $$aEBOOK
001444730 982__ $$aEbook
001444730 983__ $$aOnline
001444730 994__ $$a92$$bISE