SoundDoA: Learn Sound Source Direction of Arrival and Semantics from Sound Raw Waveforms

Abstract

A fundamental task for an agent to understand an environment acoustically is to detect sound source location (like direction of arrival (DoA)) and semantic label. It is a challenging task: firstly, sound sources overlap in time, frequency and space; secondly, while semantics are largely conveyed through time-frequency energy (amplitude) contours, DoA is encoded in inter-channel phase difference; lastly, although the number of microphone sensors are sparse, recorded sound waveform is temporally dense due to the high sampling rates. Existing methods for predicting DoA mostly depend on pre-extracted 2D acoustic feature such as GCC-PHAT and Mel-spectrograms so as to benefit from the success of mature 2D image based deep neural networks. We instead propose a novel end-to-end trainable framework, named SoundDoA , that is capable of learning sound source DoA and semantics directly from sound raw waveforms. We first use a learnable front-end filter bank to dynamically encode sound source semantics and DoA relevant features into a compact representation. A backbone network consisting of two identical sub-networks with layerwise communication strategy is then proposed to further learn semantic label and DoA both separately and jointly. Finally, a permutation invariant multi-track head is added to regress DoA and classify semantic label. Extensive experimental results on DCASE 2020 sound event detection and localization dataset (SELD) demonstrate the superiority of SoundDoA , when comparing with other existing methods.