  In this work, we propose a multi-head relevance weighting framework to learn
audio representations from raw waveforms. The audio waveform, split into
windows of short duration, are processed with a 1-D convolutional layer of
cosine modulated Gaussian filters acting as a learnable filterbank. The key
novelty of the proposed framework is the introduction of multi-head relevance
on the learnt filterbank representations. Each head of the relevance network is
modelled as a separate sub-network. These heads perform representation
enhancement by generating weight masks for different parts of the
time-frequency representation learnt by the parametric acoustic filterbank
layer. The relevance weighted representations are fed to a neural classifier
and the whole system is trained jointly for the audio classification objective.
Experiments are performed on the DCASE2020 Task 1A challenge as well as the
Urban Sound Classification (USC) tasks. In these experiments, the proposed
approach yields relative improvements of 10% and 23% respectively for the
DCASE2020 and USC datasets over the mel-spectrogram baseline. Also, the
analysis of multi-head relevance weights provides insights on the learned
representations.
