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Temporal Acoustic Words for Online Acoustic Event Detection

  • Rene Grzeszick
  • Axel Plinge
  • Gernot A. Fink
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

The Bag-of-Features principle proved successful in many pattern recognition tasks ranging from document analysis and image classification to gesture recognition and even forensic applications. Lately these methods emerged in the field of acoustic event detection and showed very promising results. The detection and classification of acoustic events is an important task for many practical applications like video understanding, surveillance or speech enhancement. In this paper a novel approach for online acoustic event detection is presented that builds on top of the Bag-of-Features principle. Features are calculated for all frames in a given window. Applying the concept of feature augmentation additional temporal information is encoded in each feature vector. These feature vectors are then softly quantized so that a Bag-of-Feature representation is computed. These representations are evaluated by a classifier in a sliding window approach. The experiments on a challenging indoor dataset of acoustic events will show that the proposed method yields state-of-the-art results compared to other online event detection methods. Furthermore, it will be shown that the temporal feature augmentation significantly improves the recognition rates.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.Department of Computer ScienceTU DortmundDortmundGermany

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