Sound Event Localization and Detection Using CRNN on Pairs of Microphones
This work addresses sound event localization and detection for audio processing applications, but it is incremental as it builds on existing CRNN methods.
The paper tackles sound event localization and detection from multichannel recordings by using two CRNNs for SED and TDOA estimation on microphone pairs, and it outperforms the DCASE 2019 baseline system.
This paper proposes sound event localization and detection methods from multichannel recording. The proposed system is based on two Convolutional Recurrent Neural Networks (CRNNs) to perform sound event detection (SED) and time difference of arrival (TDOA) estimation on each pair of microphones in a microphone array. In this paper, the system is evaluated with a four-microphone array, and thus combines the results from six pairs of microphones to provide a final classification and a 3-D direction of arrival (DOA) estimate. Results demonstrate that the proposed approach outperforms the DCASE 2019 baseline system.