Device Invariance using Domain Adaptation on Acoustic Scene Classification

2026-07-28

AI Summary

This study addresses the performance degradation in acoustic scene classification caused by variations in recording devices. To mitigate this issue, the authors integrate CNN and Transformer architectures as feature extractors and systematically evaluate the effectiveness of two domain adaptation methods—Domain-Adversarial Neural Networks (DANN) and Conditional Domain Adversarial Networks (CDAN)—in multi-device scenarios. Experiments conducted on the DCASE 2020 multi-device dataset demonstrate that DANN consistently improves performance across both CNN- and Transformer-based features, whereas CDAN yields gains only with CNN-derived representations. These findings underscore the necessity of co-designing domain adaptation strategies with the underlying feature representation and provide clear practical guidance for selecting appropriate methods to achieve device-invariant acoustic scene classification.

Abstract

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.

Problem

• device invariance
• domain adaptation
• acoustic scene classification
• domain shift
• cross-device robustness

Innovation

• domain adaptation
• acoustic scene classification
• device invariance
• DANN
• CDAN