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Connectionist Temporal Localization for Sound Event Detection with Sequential Labeling

Citation Author(s):
Yun Wang, Florian Metze
Submitted by:
Yun Wang
Last updated:
8 May 2019 - 11:49pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Juncheng Li
Paper Code:
1551
Categories:

Abstract 

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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