A consecutive ensemble of four CRNN models, predicting source count, then directions of arrival, then classes, achieves top results on the DCASE 2019 sound event localization and detection task.
Sound source detection, localization and classification using consecutive ensemble of CRNN models
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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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Sound source detection, localization and classification using consecutive ensemble of CRNN models
A consecutive ensemble of four CRNN models, predicting source count, then directions of arrival, then classes, achieves top results on the DCASE 2019 sound event localization and detection task.