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