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Many state-of-the-art systems for audio tagging and sound event detectionemploy convolutional recurrent neural architectures. Typically, they aretrained in a mean teacher setting to deal with the heterogeneous annotation ofthe available data.  In this work, we present a thorough analysis of how changing the temporalresolution of these convolutional recurrent neural networks - which can be doneby simply adapting their pooling operations - impacts their performance. Byusing a variety of evaluation metrics, we investigate the effects of adaptingthis design parameter under several sound recognition scenarios involvingdifferent needs in terms of temporal localization.

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