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Conference Paper
Sound Event Localization and Detection using Spatial Feature Fusion
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- Authors
- Su-Hwa Jo, Chi Yoon Jeong, Mooseop Kim
- Issue Date
- 2022-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1849-1851
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Sound event localization and detection (SELD) can identify the category and location of a sound event along with providing valuable information for many applications. Existing methods primarily use convolutional recurrent neural networks as a network model and Log-Mel spectrograms to classify sound events. However, there are no dominant spatial features to identify the direction of sound events. The fusion of various spatial features can lead to a better performance in the SELD task. In this study, we propose an optimal feature fusion by systematically analyzing various combinations of spatial features. We used the TAU-NIGENS spatial sound events 2021 dataset to evaluate the SELD task performance of various combinations of spatial features. We found that the combination of interaural phase difference (IPD) and sinIPD had a better performance than the other features and combinations. Finally, we confirmed that the proposed features had a better performance than the state-of-the-art methods.
- KSP Keywords
- Feature fusion, Network Model, Optimal feature, Phase Difference, Spatial Sound, event localization, neural network(NN), recurrent neural network(RNN), sound events, spatial feature, state-of-The-Art