Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection

Sandeep Kothinti, Keisuke Imoto, Debmalya Chakrabarty, Gregory Sell, Shinji Watanabe, Mounya Elhilali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Sound event detection is a challenging task, especially for scenes with multiple simultaneous events. While event classification methods tend to be fairly accurate, event localization presents additional challenges, especially when large amounts of labeled data are not available. Task4 of the 2018 DCASE challenge presents an event detection task that requires accuracy in both segmentation and recognition of events while providing only weakly labeled training data. Supervised methods can produce accurate event labels but are limited in event segmentation when training data lacks event timestamps. On the other hand, unsupervised methods that model the acoustic properties of the audio can produce accurate event boundaries but are not guided by the characteristics of event classes and sound categories. We present a hybrid approach that combines an acoustic-driven event boundary detection and a supervised label inference using a deep neural network. This framework leverages benefits of both unsupervised and supervised methodologies and takes advantage of large amounts of unlabeled data, making it ideal for large-scale weakly la-beled event detection. Compared to a baseline system, the proposed approach delivers a 15% absolute improvement in F-score, demonstrating the benefits of the hybrid bottom-up, top-down approach.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages36-40
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 2019 May 1
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 2019 May 122019 May 17

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period19/5/1219/5/17

Keywords

  • conditional restricted Boltzmann machine
  • restricted Boltzmann machine
  • Sound event detection
  • unsupervised learning
  • weakly labeled data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kothinti, S., Imoto, K., Chakrabarty, D., Sell, G., Watanabe, S., & Elhilali, M. (2019). Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 36-40). [8682772] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682772