Segment Relevance Estimation for Audio Analysis and Weakly-Labelled Classification

Juliano Henrique Foleiss; Tiago Fernandes Tavares

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arXiv · 1911.04666

Segment Relevance Estimation for Audio Analysis and Weakly-Labelled Classification

Juliano Henrique Foleiss·Tiago Fernandes Tavares

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Abstract

We propose a method that quantifies the importance, namely relevance, of audio segments for classification in weakly-labelled problems. It works by drawing information from a set of class-wise one-vs-all classifiers. By selecting the classifiers used in each specific classification problem, the relevance measure adapts to different user-defined viewpoints without requiring additional neural network training. This characteristic allows the relevance measure to highlight audio segments that quickly adapt to user-defined criteria. Such functionality can be used for computer-assisted audio analysis. Also, we propose a neural network architecture, namely RELNET, that leverages the relevance measure for weakly-labelled audio classification problems. RELNET was evaluated in the DCASE2018 dataset and achieved competitive classification results when compared to previous attention-based proposals.

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BibTeX RIS

Juliano Henrique Foleiss, Tiago Fernandes Tavares. 2019-11-12. Segment Relevance Estimation for Audio Analysis and Weakly-Labelled Classification. https://arxiv.org/abs/1911.04666

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