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

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arxiv 2011.00801 v1 pith:JEDHJRWD submitted 2020-11-02 cs.SD eess.AS

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

Nicolas Turpault (MULTISPEECH) , Romain Serizel (MULTISPEECH) , Scott Wisdom , Hakan Erdogan , John Hershey , Eduardo Fonseca , Prem Seetharaman , Justin Salamon This is my paper
classification cs.SD eess.AS
keywords soundeventdetectioneventssystemstimebenchmarknon-target
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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