Multi-level Attention Model for Weakly Supervised Audio Classification

• Main Authors: Yu, Changsong, Barsim, Karim Said, Kong, Qiuqiang, Yang, Bin
• Format: Journal Article
• Language: English
• Published: 06-03-2018
• Subjects: Computer Science - Sound
• DOI: 10.48550/arxiv.1803.02353

In this paper, we propose a multi-level attention model to solve the weakly
labelled audio classification problem. The objective of audio classification is
to predict the presence or absence of audio events in an audio clip. Recently,
Google published a large scale weakly labelled dataset called Audio Set, where
each audio clip contains only the presence or absence of the audio events,
without the onset and offset time of the audio events. Our multi-level
attention model is an extension to the previously proposed single-level
attention model. It consists of several attention modules applied on
intermediate neural network layers. The output of these attention modules are
concatenated to a vector followed by a multi-label classifier to make the final
prediction of each class. Experiments shown that our model achieves a mean
average precision (mAP) of 0.360, outperforms the state-of-the-art single-level
attention model of 0.327 and Google baseline of 0.314.