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Sound event detection (SED) and acoustic scene classification (ASC) are majortasks in environmental sound analysis. Considering that sound events and scenesare closely related to each other, some works have addressed joint analyses ofsound events and acoustic scenes based on multitask learning (MTL), in whichthe knowledge of sound events and scenes can help in estimating them mutually.The conventional MTL-based methods utilize one-hot scene labels to train therelationship between sound events and scenes; thus, the conventional methodscannot model the extent to which sound events and scenes are related. However,in the real environment, common sound events may occur in some acoustic scenes;on the other hand, some sound events occur only in a limited acoustic scene. Inthis paper, we thus propose a new method for SED based on MTL of SED and ASCusing the soft labels of acoustic scenes, which enable us to model the extentto which sound events and scenes are related. Experiments conducted using TUTSound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets show that theproposed method improves the SED performance by 3.80% in F-score compared withconventional MTL-based SED.

Sound Event Detection by Multitask Learning of Sound Events and Scenes with Soft Scene Labels