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

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