Using sequential information in polyphonic sound event detection


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Huang, Guangpu; Heittola, Toni; Virtanen, Tuomas

Abstract

To detect the class, and start and end times of sound events in real world recordings is a challenging task. Current computer systems often show relatively high frame-wise accuracy but low event-wise accuracy. In this paper, we attempted to merge the gap by explicitly including sequential information to improve the performance of a state-of-the-art polyphonic sound event detection system. We propose to 1) use delayed predictions of event activities as additional input features that are fed back to the neural network; 2) build N-grams to model the co-occurrence probabilities of different events; 3) use se-quentialloss to train neural networks. Our experiments on a corpus of real world recordings show that the N-grams could smooth the spiky output of a state-of-the-art neural network system, and improve both the frame-wise and the event-wise metrics.

Keywords

Language modelling; Polyphonic sound event detection; Sequential information

Research areas

Year:
2018
Book title:
16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018
Pages:
291-295
Month:
11
DOI:
10.1109/IWAENC.2018.8521367