Environmental Sound Classification with Parallel Temporal-spectral Attention

Understand

Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC).

• Recently, temporal attention mechanisms have been used in CNN to capture the useful information from the relevant time frames for audio classification, especially for weakly labelled data where the onset and offset times of the sound events are not applied.
• In these methods, however, the inherent spectral characteristics and variations are not explicitly exploited when obtaining the deep features.
• In this paper, we propose a novel parallel temporal-spectral attention mechanism for CNN to learn discriminative sound representations, which enhances the temporal and spectral features by capturing the importance of different time frames and frequency bands.