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A multi-room reverberant dataset for sound event localization and detection

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arxiv 1905.08546 v2 pith:5CZFLVUW submitted 2019-05-21 cs.SD eess.AS

Sharath Adavanne , Archontis Politis , Tuomas Virtanen This is my paper · ORCID
classification cs.SDeess.AS
keywords soundeventdatasetseldbenchmarkchallengedetectionlocalization
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A hybrid parametric-deep learning approach for sound event localization and detection

    cs.SD 2019-08 conditional novelty 6.0 of 10

    A hybrid parametric-deep learning system for sound event localization achieves 9.3 degrees direction error on DCASE2019 Task 3, a 2.6x improvement over the baseline, with comparable sound event detection but lower fra...

  2. Sound source detection, localization and classification using consecutive ensemble of CRNN models

    eess.AS 2019-08 conditional novelty 5.0 of 10

    A consecutive ensemble of four CRNN models, predicting source count, then directions of arrival, then classes, achieves top results on the DCASE 2019 sound event localization and detection task.

  3. A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods

    cs.RO 2025-07 unverdicted novelty 2.0 of 10

    A robotics-focused review of sound source localization research, emphasizing deep learning architectures, datasets, and open challenges.

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