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Real-time Speech and Music Classification by Large Audio Feature Space Extraction: Springer Theses

Autor Florian Eyben
en Limba Engleză Hardback – 6 ian 2016
This book reports on an outstanding thesis thathas significantly advanced the state-of-the-art in the automated analysis andclassification of speech and music.  Itdefines several standard acoustic parameter sets and describes theirimplementation in a novel, open-source, audio analysis framework calledopenSMILE, which has been accepted and intensively used worldwide. The bookoffers extensive descriptions of key methods for the automatic classificationof speech and music signals in real-life conditions and reports on theevaluation of the framework developed and the acoustic parameter sets that wereselected. It is not only intended as a manual for openSMILE users, but also andprimarily as a guide and source of inspiration for students and scientists involvedin the design of speech and music analysis methods that can robustly handlereal-life conditions.
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Specificații

ISBN-13: 9783319272986
ISBN-10: 3319272985
Pagini: 340
Ilustrații: XXXVIII, 298 p. 41 illus., 39 illus. in color.
Dimensiuni: 155 x 235 x 21 mm
Greutate: 0.65 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Springer Theses

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

Abstract.- Introduction.- Acoustic Features and Modelling.- Standard Baseline Feature Sets.- Real-time Incremental Processing.- Real-life Robustness.- Evaluation.- Discussion and Outlook.- Appendix.- Mel-frequency Filterbank Parameters.

Textul de pe ultima copertă

This book reports on an outstanding thesis thathas significantly advanced the state-of-the-art in the automated analysis andclassification of speech and music.  Itdefines several standard acoustic parameter sets and describes theirimplementation in a novel, open-source, audio analysis framework calledopenSMILE, which has been accepted and intensively used worldwide. The bookoffers extensive descriptions of key methods for the automatic classificationof speech and music signals in real-life conditions and reports on theevaluation of the framework developed and the acoustic parameter sets that wereselected. It is not only intended as a manual for openSMILE users, but also andprimarily as a guide and source of inspiration for students and scientists involvedin the design of speech and music analysis methods that can robustly handlereal-life conditions.

Caracteristici

Nominated as an outstanding thesis by Technische Universität München, Germany Describes the details and architecture of openSMILE - the number 1 open-source toolkit in speech emotion analytics and computational paralinguistics Reports on extensive automaticclassification results for over ten public speech and music databases Includes supplementary material: sn.pub/extras