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Conference Papers Year : 2020

Training Sound Event Detection On A Heterogeneous Dataset


Training a sound event detection algorithm on a heterogeneous dataset including both recorded and synthetic soundscapes that can have various labeling granularity is a non-trivial task that can lead to systems requiring several technical choices. These technical choices are often passed from one system to another without being questioned. We propose to perform a detailed analysis of DCASE 2020 task 4 sound event detection baseline with regards to several aspects such as the type of data used for training, the parameters of the mean-teacher or the transformations applied while generating the synthetic soundscapes. Some of the parameters that are usually used as default are shown to be sub-optimal.
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Dates and versions

hal-02891665 , version 1 (07-07-2020)
hal-02891665 , version 2 (10-09-2020)



Nicolas Turpault, Romain Serizel. Training Sound Event Detection On A Heterogeneous Dataset. DCASE Workshop, Nov 2020, Tokyo, Japan. ⟨hal-02891665v2⟩
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