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Impact of temporal resolution on convolutional recurrent networks for audio tagging and sound event detection

2022-09-26 16:49:12
Wim Boes, Hugo Van hamme
arXiv_SD
         
Abstract

Many state-of-the-art systems for audio tagging and sound event detection employ convolutional recurrent neural architectures. Typically, they are trained in a mean teacher setting to deal with the heterogeneous annotation of the available data. In this work, we present a thorough analysis of how changing the temporal resolution of these convolutional recurrent neural networks - which can be done by simply adapting their pooling operations - impacts their performance. By using a variety of evaluation metrics, we investigate the effects of adapting this design parameter under several sound recognition scenarios involving different needs in terms of temporal localization.

Abstract (translated)

URL

https://arxiv.org/abs/2209.12843

PDF

https://arxiv.org/pdf/2209.12843.pdf

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