  In this paper, we present a novel deep fusion architecture for audio
classification tasks. The multi-channel model presented is formed using deep
convolution layers where different acoustic features are passed through each
channel. To enable dissemination of information across the channels, we
introduce attention feature maps that aid in the alignment of frames. The
output of each channel is merged using interaction parameters that non-linearly
aggregate the representative features. Finally, we evaluate the performance of
the proposed architecture on three benchmark datasets:- DCASE-2016 and LITIS
Rouen (acoustic scene recognition), and CHiME-Home (tagging). Our experimental
results suggest that the architecture presented outperforms the standard
baselines and achieves outstanding performance on the task of acoustic scene
recognition and audio tagging.
