A multi-room reverberant dataset for sound event localization and detection
By Sharath Adavanne, Archontis Politis, Tuomas Virtanen. 2019
125 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Signal Processing
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In Artificial Intelligence
- Xavier Serra
- Eduardo Fonseca
- Jordi Pons
- Xavier Favory
- Frederic Font
- Yossi Adi
- Lauri Juvela
- Paavo Alku
In Signal Processing
- Eduardo Fonseca
- Xavier Serra
- Frederic Font
- Xavier Favory
- Jordi Pons
- Andrés Ferraro
- Jakob Abeßer
- D. Ellis
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “Other instances of this approach include TAU Spatial Sound Events 2019 [29], for sound event detection and localization, and VOICe [30] for the study of domain adaptation in SED.”
- A Survey of Sound Source Localization with Deep Learning Methods (2021, unknown): “We believe these tables can be very useful for a quick search of methods with a given set of characteristics.”