skip to main content
10.1145/2733373.2806390acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
short-paper

ESC: Dataset for Environmental Sound Classification

Published:13 October 2015Publication History

ABSTRACT

One of the obstacles in research activities concentrating on environmental sound classification is the scarcity of suitable and publicly available datasets. This paper tries to address that issue by presenting a new annotated collection of 2000 short clips comprising 50 classes of various common sound events, and an abundant unified compilation of 250000 unlabeled auditory excerpts extracted from recordings available through the Freesound project. The paper also provides an evaluation of human accuracy in classifying environmental sounds and compares it to the performance of selected baseline classifiers using features derived from mel-frequency cepstral coefficients and zero-crossing rate.

References

  1. BBC sound effects library. http://www.sound-ideas.com/sound-effects/bbc-sound-effects.html. (Aug. 5, 2015).Google ScholarGoogle Scholar
  2. E. Alexandre et al. Feature selection for sound classification in hearing aids through restricted search driven by genetic algorithms. IEEE Transactions on Audio, Speech, and Language Processing, 15(8):2249--2256, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Ballan et al. Deep networks for audio event classification in soccer videos. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 474--477, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. Barchiesi et al. Acoustic scene classification: Classifying environments from the sounds they produce. Signal Processing Magazine, 32(3):16--34, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  5. S. Chachada and C.-C. J. Kuo. Environmental sound recognition: A survey. APSIPA Transactions on Signal and Information Processing, 3:e14, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  6. F. Font, G. Roma, and X. Serra. Freesound technical demo. In Proceedings of the ACM International Conference on Multimedia, pages 411--412. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Giannoulis et al. Detection and classification of acoustic scenes and events: An IEEE AASP challenge. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  8. I. Lallemand, D. Schwarz, and T. Artieres. Content-based retrieval of environmental sounds by multiresolution analysis. In Proceedings of the Sound and Music Computing conference, 2012.Google ScholarGoogle Scholar
  9. K. Łopatka, P. Zwan, and A. Czy\.zewski. Dangerous sound event recognition using support vector machine classifiers. In Advances in Multimedia and Network Information System Technologies, pages 49--57. Springer, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  10. J. Maxime et al. Sound representation and classification benchmark for domestic robots. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 6285--6292. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  11. T. Nishiura and S. Nakamura. An evaluation of sound source identification with RWCP sound scene database in real acoustic environments. In Proceedings of the IEEE International Conference on Multimedia and Expo, volume 2, pages 265--268. IEEE, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  12. K. J. Piczak. Environmental sound classification with convolutional neural networks. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2015.textitIn press.Google ScholarGoogle ScholarCross RefCross Ref
  13. A. Plinge et al. A bag-of-features approach to acoustic event detection. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3704--3708. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Salamon, C. Jacoby, and J. P. Bello. A dataset and taxonomy for urban sound research. In Proceedings of the ACM International Conference on Multimedia, pages 1041--1044. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Stowell and M. D. Plumbley. An open dataset for research on audio field recording archives: freefield1010. arXiv preprint arXiv:1309.5275, 2013.Google ScholarGoogle Scholar
  16. M. Vacher, J.-F. Serignat, and S. Chaillol. Sound classification in a smart room environment: an approach using GMM and HMM methods. In Proceedings of the IEEE Conference on Speech Technology and Human-Computer Dialogue, pages 135--146, 2007.Google ScholarGoogle Scholar
  17. M. van Grootel, T. Andringa, and J. Krijnders. DARES-G1: Database of annotated real-world everyday sounds. In Proceedings of the NAG/DAGA International Conference on Acoustics, 2009.Google ScholarGoogle Scholar

Index Terms

  1. ESC: Dataset for Environmental Sound Classification

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          MM '15: Proceedings of the 23rd ACM international conference on Multimedia
          October 2015
          1402 pages
          ISBN:9781450334594
          DOI:10.1145/2733373

          Copyright © 2015 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 13 October 2015

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • short-paper

          Acceptance Rates

          MM '15 Paper Acceptance Rate56of252submissions,22%Overall Acceptance Rate995of4,171submissions,24%

          Upcoming Conference

          MM '24
          MM '24: The 32nd ACM International Conference on Multimedia
          October 28 - November 1, 2024
          Melbourne , VIC , Australia

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader