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

  • Florian Eyben

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxxviii
  2. Florian Eyben
    Pages 1-7
  3. Florian Eyben
    Pages 9-122
  4. Florian Eyben
    Pages 123-137
  5. Florian Eyben
    Pages 139-161
  6. Florian Eyben
    Pages 163-183
  7. Florian Eyben
    Pages 185-236
  8. Florian Eyben
    Pages 237-245
  9. Back Matter
    Pages 247-298

About this book

Introduction

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.

Keywords

openSMILE Speech Emotion Recognition Voice Analytics Affective Computing Acoustic Feature Extraction Computational Paralinguistics Music Information Retrieval

Authors and affiliations

  • Florian Eyben
    • 1
  1. 1.Institute for Human-Machine Communication (MMK)Technische Universität MünchenMunichGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-27299-3
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-27298-6
  • Online ISBN 978-3-319-27299-3
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • Buy this book on publisher's site
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