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While end-to-end systems are becoming popular in auditory signal processingincluding automatic music tagging, models using raw audio as input needs alarge amount of data and computational resources without domain knowledge.Inspired by the fact that temporal modulation is regarded as an essentialcomponent in auditory perception, we introduce the Temporal Modulation NeuralNetwork (TMNN) that combines Mel-like data-driven front ends and temporalmodulation filters with a simple ResNet back end. The structure includes a setof temporal modulation filters to capture long-term patterns in all frequencychannels. Experimental results show that the proposed front ends surpassstate-of-the-art (SOTA) methods on the MagnaTagATune dataset in automatic musictagging, and they are also helpful for keyword spotting on speech commands.Moreover, the model performance for each tag suggests that genre or instrumenttags with complex rhythm and mood tags can especially be improved with temporalmodulation.

Learnable Front Ends Based on Temporal Modulation for Music Tagging