Acoustic Features Fusion using Attentive Multi-channel Deep Architecture
This work addresses audio classification problems for researchers and practitioners, but it appears incremental as it builds on existing deep learning methods with specific architectural improvements.
The paper tackles audio classification by proposing a deep fusion architecture that uses attention to align acoustic features across multiple channels, achieving outstanding performance on acoustic scene recognition and audio tagging tasks.
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.