# Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification

arXiv: https://arxiv.org/abs/1904.10678 · HTML: https://mlsanity.com/paper/1904.10678/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [Unsupervised Model Adaptation for Source-free Segmentation of Medical Images](https://mlsanity.com/paper/2211.00807/) (2022)
- [Domain Adaptation for the Segmentation of Confidential Medical Images](https://mlsanity.com/paper/2101.00522/) (2021)
- [Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations](https://mlsanity.com/paper/2110.01506/) (2021)
- [Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit](https://mlsanity.com/paper/2106.09539/) (2021)
- [Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices](https://mlsanity.com/paper/2110.09598/) (2021)
- [Domain Information Control at Inference Time for Acoustic Scene Classification](https://mlsanity.com/paper/2306.08010/) (2023)
- [Visual and audio scene classification for detecting discrepancies in video: a baseline method and experimental protocol](https://mlsanity.com/paper/2405.00384/) (2024)
- [Device Invariance using Domain Adaptation on Acoustic Scene Classification](https://mlsanity.com/paper/2607.25887/) (2026)

## Abstract (arXiv)

A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of HDeltaH-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation, and we present an adversarial training based method using the Wasserstein distance. We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32% to 45%, using the TUT Acoustic Scenes dataset.