# Device Invariance using Domain Adaptation on Acoustic Scene Classification

arXiv: https://arxiv.org/abs/2607.25887 · HTML: https://mlsanity.com/paper/2607.25887/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Efficient Training of Audio Transformers with Patchout](https://mlsanity.com/paper/2110.05069/) (2021)
- [Gradually Vanishing Bridge for Adversarial Domain Adaptation](https://mlsanity.com/paper/2003.13183/) (2020)
- [Cycle Self-Training for Domain Adaptation](https://mlsanity.com/paper/2103.03571/) (2021)
- [Delving Deep into the Generalization of Vision Transformers under Distribution Shifts](https://mlsanity.com/paper/2106.07617/) (2021)
- [Masked Modeling Duo: Towards a Universal Audio Pre-training Framework](https://mlsanity.com/paper/2404.06095/) (2024)
- [Unsupervised adversarial domain adaptation for acoustic scene classification](https://mlsanity.com/paper/1808.05777/) (2018)
- [Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification](https://mlsanity.com/paper/1904.10678/) (2019)
- [CAA-Net: Conditional Atrous CNNs with Attention for Explainable Device-robust Acoustic Scene Classification](https://mlsanity.com/paper/2011.09299/) (2020)
- [Unsupervised Multi-Target Domain Adaptation for Acoustic Scene Classification](https://mlsanity.com/paper/2105.10340/) (2021)

## Abstract (arXiv)

This paper explores the effectiveness of domain adaptation techniques when using convolutional neural network (CNN)-based and transformer-based feature representations for acoustic scene classification. Two well-known domain adaptation techniques, namely domain adversarial neural network (also called DANN) and conditional domain adversarial network (also called CDAN) are evaluated under various domain shifts. Our study indicates that DANN provides effective domain adaptation fairly consistently for both feature extractors. On the other hand, CDAN provides effective domain adaptation only for CNN-based feature extractors. The study gives insights into how domain adaptation methods may need to be tailored to the underlying feature representation. Experimental evaluation with multiple devices on the DCASE 2020 dataset supports the observations.