Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

Abstract

The deployment of machine listening algorithms in real-life applications is often impeded by a domain shift caused for instance by different microphone characteristics. In this paper, we propose a novel domain adaptation strategy based on disentanglement learning. The goal is to disentangle task-specific and domain-specific characteristics in the analyzed audio recordings. In particular, we combine two strategies: First, we apply different binary masks to internal embedding representations and, second, we suggest a novel combination of categorical cross-entropy and variance-based losses. Our results confirm the disentanglement of both tasks on an embedding level but show only minor improvement in the acoustic scene classification performance, when training data from both domains can be used. As a second finding, we can confirm the effectiveness of a state-of-the-art unsupervised domain adaptation strategy, which performs across-domain adaptation on a feature-level instead.

CCSRD: Content-Centric Speech Representation Disentanglement Learning for End-to-End Speech Translation

“…Although disentangled speech representation learning is not new in the community of speech processing (Xie et al, 2021;Abeßer and Müller, 2021), to the best of our knowledge, this is the first attempt to learn disentangled speech representations for end-to-end speech translation. In a nutshell, our contributions are listed as follows.…”