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Acoustic event detection and scene classification are major research tasks inenvironmental sound analysis, and many methods based on neural networks havebeen proposed. Conventional methods have addressed these tasks separately;however, acoustic events and scenes are closely related to each other. Forexample, in the acoustic scene `office', the acoustic events `mouse clicking'and `keyboard typing' are likely to occur. In this paper, we propose multitasklearning for joint analysis of acoustic events and scenes, which shares theparts of the networks holding information on acoustic events and scenes incommon. By integrating the two networks, we expect that information on acousticscenes will improve the performance of acoustic event detection. Experimentalresults obtained using TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016datasets indicate that the proposed method improves the performance of acousticevent detection by 10.66 percentage points in terms of the F-score, comparedwith a conventional method based on a convolutional recurrent neural network.

Joint Analysis of Acoustic Events and Scenes Based on Multitask Learning