Connectionist Temporal Localization for Sound Event Detection with Sequential Labeling

Yun Wang, Florian Metze
10/22/2018
cs.SDeess.AS

Official code: MaigoAkisame/cmu-thesis (170 stars, MIT, last commit 2022-05-14).

Abstract

Research on sound event detection (SED) with weak labeling has mostly focused on presence/absence labeling, which provides no temporal information at all about the event occurrences. In this paper, we consider SED with sequential labeling, which specifies the temporal order of the event boundaries. The conventional connectionist temporal classification (CTC) framework, when applied to SED with sequential labeling, does not localize long events well due to a "peak clustering" problem. We adapt the CTC framework and propose connectionist temporal localization (CTL), which successfully solves the problem. Evaluation on a subset of Audio Set shows that CTL closes a third of the gap between presence/ absence labeling and strong labeling, demonstrating the usefulness of the extra temporal information in sequential labeling. CTL also makes it easy to combine sequential labeling with presence/absence labeling and strong labeling.

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Code Implementations(1)

MaigoAkisame/cmu-thesisOfficial100%
MIT

Code for Yun Wang's PhD Thesis: Polyphonic Sound Event Detection with Weak Labeling

17047Shell, PythonOct 22, 20184 years agoMIT

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Cite this paper

@article{wang2018connectionist,
  title  = {Connectionist Temporal Localization for Sound Event Detection with Sequential Labeling},
  author = {Yun Wang and Florian Metze},
  year   = {2018},
  eprint = {1810.09052},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1810.09052}
}

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