Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification
This work addresses the problem of adapting acoustic scene classification systems to new recording devices for researchers and practitioners in machine listening, representing an incremental improvement over previous adversarial methods.
The paper tackles the performance degradation of acoustic scene classification systems when using data from unseen recording conditions by proposing an unsupervised adversarial domain adaptation method based on the Wasserstein distance, improving mean accuracy from 32% to 45% on the TUT Acoustic Scenes dataset.
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.