# Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

arXiv: https://arxiv.org/abs/2110.13586 · HTML: https://mlsanity.com/paper/2110.13586/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2021.

## Built on by (most cited first, 2 of 2)

- [VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data](https://mlsanity.com/paper/2207.09204/) (2022)
- [Adversarial Training Improves Generalization Under Distribution Shifts in Bird Sound Classification](https://mlsanity.com/paper/2507.13727/) (2025)

## Abstract (arXiv)

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.