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arXiv · 1908.00766

Sound source detection, localization and classification using consecutive ensemble of CRNN models

Sławomir Kapka·Mateusz Lewandowski
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Abstract

In this paper, we describe our method for DCASE2019 task3: Sound Event Localization and Detection (SELD). We use four CRNN SELDnet-like single output models which run in a consecutive manner to recover all possible information of occurring events. We decompose the SELD task into estimating number of active sources, estimating direction of arrival of a single source, estimating direction of arrival of the second source where the direction of the first one is known and a multi-label classification task. We use custom consecutive ensemble to predict events' onset, offset, direction of arrival and class. The proposed approach is evaluated on the TAU Spatial Sound Events 2019 - Ambisonic and it is compared with other participants' submissions.

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BibTeXRIS

Sławomir Kapka, Mateusz Lewandowski. 2019-08-02. Sound source detection, localization and classification using consecutive ensemble of CRNN models. https://doi.org/10.33682/1syg-dy60

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