Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Adversarial Domain Adaptation Based On The Wasserstein
  Distance For Acoustic Scene Classification   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.

15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph