Joint Analysis of Sound Events and Acoustic Scenes Using Multitask Learning

Abstract

Sound event detection (SED) and acoustic scene classification (ASC) are important research topics in environmental sound analysis. Many research groups have addressed SED and ASC using neural-network-based methods, such as the convolutional neural network (CNN), recurrent neural network (RNN), and convolutional recurrent neural network (CRNN). The conventional methods address SED and ASC separately even though sound events and acoustic scenes are closely related to each other. For example, in the acoustic scene "office," the sound events "mouse clicking" and "keyboard typing" are likely to occur. Therefore, it is expected that information on sound events and acoustic scenes will be of mutual aid for SED and ASC. In this paper, we propose multitask learning for joint analysis of sound events and acoustic scenes, in which the parts of the networks holding information on sound events and acoustic scenes in common are shared. Experimental results obtained using the TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets indicate that the proposed method improves the performance of SED and ASC by 1.31 and 1.80 percentage points in terms of the F-score, respectively, compared with the conventional CRNN-based method.

arXiv commentAccepted to IEICE Transactions on Information and Systems. arXiv admin note: text overlap with arXiv:1904.12146

References in corpus (5)

• Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
• Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks
• Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments
• Surrey-cvssp system for DCASE2017 challenge task4
• Guided Learning Convolution System for DCASE 2019 Task 4

Cited by in corpus (1)

• Audio Event-Relational Graph Representation Learning for Acoustic Scene Classification