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

TL;DR

This paper investigates how acoustic scene and sound event information mutually enhance each other's detection and classification tasks using domain adversarial training and fake labels, revealing that single-task methods may implicitly leverage this mutual information.

Contribution

It provides a detailed analysis of the mutual benefits between acoustic scene classification and sound event detection, highlighting the effectiveness of domain adversarial training and fake-label methods.

Findings

Mutual information improves SED and ASC performance.

Single-task methods outperform joint methods in experiments.

Implicit mutual benefits are observed even with single-task approaches.

Abstract

Acoustic scene classification (ASC) and sound event detection (SED) are fundamental tasks in environmental sound analysis, and many methods based on deep learning have been proposed. Considering that information on acoustic scenes and sound events helps SED and ASC mutually, some researchers have proposed a joint analysis of acoustic scenes and sound events by multitask learning (MTL). However, conventional works have not investigated in detail how acoustic scenes and sound events mutually benefit SED and ASC. We, therefore, investigate the impact of information on acoustic scenes and sound events on the performance of SED and ASC by using domain adversarial training based on a gradient reversal layer (GRL) or model training with fake labels. Experimental results obtained using the TUT Acoustic Scenes 2016/2017 and TUT Sound Events 2016/2017 show that pieces of information on acoustic scenes and sound events are effectively used to detect sound events and classify acoustic scenes, respectively. Moreover, upon comparing GRL- and fake-label-based methods with single-task-based ASC and SED methods, single-task-based methods are found to achieve better performance. This result implies that even when using single-task-based ASC and SED methods, information on acoustic scenes may be implicitly utilized for SED and vice versa.