  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 H{\Delta}H-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.
