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Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes

Abstract : We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We analyze the performance of the submissions to DCASE 2021 task 4 depending on time related modifications (time position of an event and length of clips) and we study the impact of non-target sound events and reverberation. We show that the localization in time of sound events is still a problem for SED systems. We also show that reverberation and non-target sound events are severely degrading the performance of the SED systems. In the latter case, sound separation seems like a promising solution.
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https://hal.inria.fr/hal-02984675
Contributor : Romain Serizel Connect in order to contact the contributor
Submitted on : Saturday, October 31, 2020 - 6:37:54 PM
Last modification on : Friday, November 18, 2022 - 9:23:30 AM
Long-term archiving on: : Monday, February 1, 2021 - 6:16:18 PM

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Nicolas Turpault, Romain Serizel, Scott Wisdom, Hakan Erdogan, John R Hershey, et al.. Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes. ICASSP 2021 - 46th International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto/Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414789⟩. ⟨hal-02984675⟩

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