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Real-time speech and music classification by large audio feature space extraction[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.454
書名/作者:	Real-time speech and music classification by large audio feature space extraction/ by Florian Eyben.
作者:	Eyben, Florian.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xxxviii, 298 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Automatic speech recognition.
標題:	Human-computer interaction.
標題:	Engineering.
標題:	Signal, Image and Speech Processing.
標題:	User Interfaces and Human Computer Interaction.
標題:	Engineering Acoustics.
標題:	Computational Linguistics.
ISBN:	9783319272993
ISBN:	9783319272986
內容註:	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.
摘要、提要註:	This book reports on an outstanding thesis that has significantly advanced the state-of-the-art in the automated analysis and classification of speech and music. It defines several standard acoustic parameter sets and describes their implementation in a novel, open-source, audio analysis framework called openSMILE, which has been accepted and intensively used worldwide. The book offers extensive descriptions of key methods for the automatic classification of speech and music signals in real-life conditions and reports on the evaluation of the framework developed and the acoustic parameter sets that were selected. It is not only intended as a manual for openSMILE users, but also and primarily as a guide and source of inspiration for students and scientists involved in the design of speech and music analysis methods that can robustly handle real-life conditions.
電子資源:	http://dx.doi.org/10.1007/978-3-319-27299-3

Real-time speech and music classification by large audio feature space extraction[electronic resource] /
Eyben, Florian.

Real-time speech and music classification by large audio feature space extraction[electronic resource] /by Florian Eyben. - Cham :Springer International Publishing :2016. - xxxviii, 298 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

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.

This book reports on an outstanding thesis that has significantly advanced the state-of-the-art in the automated analysis and classification of speech and music. It defines several standard acoustic parameter sets and describes their implementation in a novel, open-source, audio analysis framework called openSMILE, which has been accepted and intensively used worldwide. The book offers extensive descriptions of key methods for the automatic classification of speech and music signals in real-life conditions and reports on the evaluation of the framework developed and the acoustic parameter sets that were selected. It is not only intended as a manual for openSMILE users, but also and primarily as a guide and source of inspiration for students and scientists involved in the design of speech and music analysis methods that can robustly handle real-life conditions.

ISBN: 9783319272993

Standard No.: 10.1007/978-3-319-27299-3doiSubjects--Topical Terms:

175629
Automatic speech recognition.

LC Class. No.: TK7895.S65

Dewey Class. No.: 006.454

Real-time speech and music classification by large audio feature space extraction[electronic resource] /
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