SoundDet: Polyphonic Sound Event Detection and Localization from Raw Waveform

• URL: http://arxiv.org/abs/2106.06969v1
• Date: Sun, 13 Jun 2021 11:43:41 GMT
• Title: SoundDet: Polyphonic Sound Event Detection and Localization from Raw Waveform
• Authors: Yuhang He, Niki Trigoni, Andrew Markham
• Abstract summary: SoundDet is an end-to-end trainable and light-weight framework for polyphonic moving sound event detection and localization. SoundDet directly consumes the raw, multichannel waveform and treats the temporal sound event as a complete sound-object" to be detected. A dense sound proposal event map is then constructed to handle the challenges of predicting events with large varying temporal duration.
• Score: 48.68714598985078
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a new framework SoundDet, which is an end-to-end trainable and light-weight framework, for polyphonic moving sound event detection and localization. Prior methods typically approach this problem by preprocessing raw waveform into time-frequency representations, which is more amenable to process with well-established image processing pipelines. Prior methods also detect in segment-wise manner, leading to incomplete and partial detections. SoundDet takes a novel approach and directly consumes the raw, multichannel waveform and treats the spatio-temporal sound event as a complete ``sound-object" to be detected. Specifically, SoundDet consists of a backbone neural network and two parallel heads for temporal detection and spatial localization, respectively. Given the large sampling rate of raw waveform, the backbone network first learns a set of phase-sensitive and frequency-selective bank of filters to explicitly retain direction-of-arrival information, whilst being highly computationally and parametrically efficient than standard 1D/2D convolution. A dense sound event proposal map is then constructed to handle the challenges of predicting events with large varying temporal duration. Accompanying the dense proposal map are a temporal overlapness map and a motion smoothness map that measure a proposal's confidence to be an event from temporal detection accuracy and movement consistency perspective. Involving the two maps guarantees SoundDet to be trained in a spatio-temporally unified manner. Experimental results on the public DCASE dataset show the advantage of SoundDet on both segment-based and our newly proposed event-based evaluation system.

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