We present a comparative analysis of the performance of state-of-the-art sound event detection systems. In particular, we study the robustness of the systems to noise and signal degradation, which is known to impact model generalization. Our analysis is based on the results of task 4 of the DCASE 2019 challenge, where submitted systems were evaluated on, in addition to real-world recordings, a series of synthetic soundscapes that allow us to carefully control for different soundscape characteristics. Our results show that while overall systems exhibit significant improvements compared to previous work, they still suffer from biases that could prevent them from generalizing to real-world scenarios.
New Dataset: DESED (Domestic Environment Sound Event Detection)
The study was performed using a new dataset for sound event detection, DESED:
Sound Event Detection in Synthetic Domestic Environments
R. Serizel, N. Turpault, A. Shah, J. Salamon
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
[IEEE][PDF][BibTeX][Copyright]
- Dataset website: https://project.inria.fr/desed/
- Includes both recorded and synthesized soundscapes
- Includes isolated sound events allowing users to generate new data using Scaper
Sound Event Detection in Synthetic Domestic Environments
R. Serizel, N. Turpault, A. Shah, J. Salamon
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
[IEEE][PDF][BibTeX][Copyright]
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