A Multi-room Reverberant Dataset for Sound Event Localization and Detection
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019) |
| Pages | 10-14 |
| ISBN (Electronic) | 978-0-578-59596-2 |
| Publication status | Published - Oct 2019 |
| Publication type | A4 Article in a conference publication |
| Event | Workshop on Detection and Classification of Acoustic Scenes and Events - New York, United States Duration: 25 Oct 2019 → 26 Oct 2019 |
Workshop
| Workshop | Workshop on Detection and Classification of Acoustic Scenes and Events |
|---|---|
| Abbreviated title | DCASE |
| Country | United States |
| City | New York |
| Period | 25/10/19 → 26/10/19 |
Abstract
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 where 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.