Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification


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Drossos, Konstantinos; Magron, Paul; Virtanen, Tuomas

Abstract

A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of HΔH-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation, and we present an adversarial training based method using the Wasserstein distance. We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32{\%} to 45{\%}, using the TUT Acoustic Scenes dataset.

Keywords

Acoustic scene classification; unsupervised domain adaptation; Wasserstein distance; adversarial training

Year:
2019
Book title:
2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
Series:
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Month:
10
ISBN:
978-1-7281-1124-7
DOI:
10.1109/WASPAA.2019.8937231