# Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices

arXiv: https://arxiv.org/abs/2110.09598 · HTML: https://mlsanity.com/paper/2110.09598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [Acoustic Scene Classification](https://mlsanity.com/paper/1411.3715/) (2014)
- [A Two-Stage Approach to Device-Robust Acoustic Scene Classification](https://mlsanity.com/paper/2011.01447/) (2020)
- [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)
- [Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification](https://mlsanity.com/paper/2008.00110/) (2020)
- [Domain Mismatch Robust Acoustic Scene Classification using Channel Information Conversion](https://mlsanity.com/paper/1812.01731/) (2018)
- [Acoustic Scene Classification: A Competition Review](https://mlsanity.com/paper/1808.02357/) (2018)
- [Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching](https://mlsanity.com/paper/2005.00145/) (2020)
- [Low-Resource Domain Adaptation for Speaker Recognition Using Cycle-GANs](https://mlsanity.com/paper/1910.11909/) (2019)

## Built on by (most cited first, 1 of 1)

- [Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels](https://mlsanity.com/paper/2608.14287/) (2026)

## Abstract (arXiv)

In classification tasks, the classification accuracy diminishes when the data is gathered in different domains. To address this problem, in this paper, we investigate several adversarial models for domain adaptation (DA) and their effect on the acoustic scene classification task. The studied models include several types of generative adversarial networks (GAN), with different loss functions, and the so-called cycle GAN which consists of two interconnected GAN models. The experiments are performed on the DCASE20 challenge task 1A dataset, in which we can leverage the paired examples of data recorded using different devices, i.e., the source and target domain recordings. The results of performed experiments indicate that the best performing domain adaptation can be obtained using the cycle GAN, which achieves as much as 66% relative improvement in accuracy for the target domain device, while only 6% relative decrease in accuracy on the source domain. In addition, by utilizing the paired data examples, we are able to improve the overall accuracy over the model trained using larger unpaired data set, while decreasing the computational cost of the model training.