A multi-room reverberant dataset for sound event localization and detection
This work addresses the need for standardized datasets and benchmarks in sound event localization and detection, primarily for researchers in audio signal processing, but it is incremental as part of an existing challenge setup.
The paper tackled the sound event localization and detection (SELD) task by providing a synthesized multi-room reverberant dataset with spatial coordinates, and used a baseline convolutional recurrent neural network to generate benchmark scores for the DCASE 2019 challenge.
This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge. The goal of the SELD task is to detect the temporal activities of a known set of sound event classes, and further localize them in space when active. As part of the challenge, a synthesized dataset with each sound event associated with a spatial coordinate represented using azimuth and elevation angles is provided. These sound events are spatialized using real-life impulse responses collected at multiple spatial coordinates in five different rooms with varying dimensions and material properties. A baseline SELD method employing a convolutional recurrent neural network is used to generate benchmark scores for this reverberant dataset. The benchmark scores are obtained using the recommended cross-validation setup.