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Acoustic scene classification (ASC) and sound event detection (SED) arefundamental tasks in environmental sound analysis, and many methods based ondeep learning have been proposed. Considering that information on acousticscenes and sound events helps SED and ASC mutually, some researchers haveproposed a joint analysis of acoustic scenes and sound events by multitasklearning (MTL). However, conventional works have not investigated in detail howacoustic scenes and sound events mutually benefit SED and ASC. We, therefore,investigate the impact of information on acoustic scenes and sound events onthe performance of SED and ASC by using domain adversarial training based on agradient reversal layer (GRL) or model training with fake labels. Experimentalresults obtained using the TUT Acoustic Scenes 2016/2017 and TUT Sound Events2016/2017 show that pieces of information on acoustic scenes and sound eventsare effectively used to detect sound events and classify acoustic scenes,respectively. Moreover, upon comparing GRL- and fake-label-based methods withsingle-task-based ASC and SED methods, single-task-based methods are found toachieve better performance. This result implies that even when usingsingle-task-based ASC and SED methods, information on acoustic scenes may beimplicitly utilized for SED and vice versa.

How Information on Acoustic Scenes and Sound Events Mutually Benefits Event Detection and Scene Classification Tasks