Connectionist Temporal Localization for Sound Event Detection with Sequential Labeling
This addresses the challenge of improving temporal localization in sound event detection for applications like audio analysis, though it is incremental as it adapts an existing framework.
The paper tackled the problem of sound event detection with sequential labeling, where conventional CTC frameworks poorly localize long events due to 'peak clustering', and proposed connectionist temporal localization (CTL) to solve this, closing a third of the gap between presence/absence and strong labeling on Audio Set.
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.