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

Wim Boes; Hugo Van hamme

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arXiv · 2209.12843

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

Wim Boes·Hugo Van hamme

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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.

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Wim Boes, Hugo Van hamme. 2022-09-26. Impact of temporal resolution on convolutional recurrent networks for audio tagging and sound event detection. https://arxiv.org/abs/2209.12843

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