2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-24.1
Paper Title WASSERSTEIN BARYCENTER TRANSPORT FOR ACOUSTIC ADAPTATION
Authors Eduardo Fernandes Montesuma, Universidade Federal do Ceará, Brazil; Fred-Maurice Ngolè Mboula, Université Paris-Saclay, France
SessionMLSP-24: Applications in Audio and Speech Processing
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-TRL] Transfer learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The recognition of music genre and the discrimination between music and speech are important components of modern digital music systems. Depending on the acquisition conditions, such as background environment, these signals may come from different probability distributions, making the learning problem complicated. In this context, domain adaptation is a key theory to improve performance. Considering data coming from various background conditions, the adaptation scenario is called multi-source. This paper proposes a multi-source domain adaptation algorithm called Wasserstein Barycenter Transport, which transports the source domains to a target domain by creating an intermediate domain using the Wasserstein barycenter. Our method outperforms other state-of-the-art algorithms, and performs better than classifiers trained with target-only data.