Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks

TL;DR

This paper introduces a convolutional recurrent neural network that jointly localizes and detects overlapping sound events in 3D space, demonstrating robustness across various array formats and real-world conditions.

Contribution

The paper presents a novel CNN-RNN model that simultaneously performs sound event detection and 3D localization without array-specific feature extraction, applicable to diverse array geometries.

Findings

Higher recall of estimated DOAs compared to baselines.

Robust performance in reverberant and low SNR environments.

Effective in scenarios with multiple overlapping sound events.

Abstract

In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3D) space. The proposed network takes a sequence of consecutive spectrogram time-frames as input and maps it to two outputs in parallel. As the first output, the sound event detection (SED) is performed as a multi-label classification task on each time-frame producing temporal activity for all the sound event classes. As the second output, localization is performed by estimating the 3D Cartesian coordinates of the direction-of-arrival (DOA) for each sound event class using multi-output regression. The proposed method is able to associate multiple DOAs with respective sound event labels and further track this association with respect to time. The proposed method uses separately the phase and magnitude component of the spectrogram calculated on each audio channel as the feature, thereby avoiding any method- and array-specific feature extraction. The method is evaluated on five Ambisonic and two circular array format datasets with different overlapping sound events in anechoic, reverberant and real-life scenarios. The proposed method is compared with two SED, three DOA estimation, and one SELD baselines. The results show that the proposed method is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios. The proposed method achieved a consistently higher recall of the estimated number of DOAs across datasets in comparison to the best baseline. Additionally, this recall was observed to be significantly better than the best baseline method for a higher number of overlapping sound events.