# Unsupervised adversarial domain adaptation for acoustic scene classification

arXiv: https://arxiv.org/abs/1808.05777 · HTML: https://mlsanity.com/paper/1808.05777/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2018.

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

- [ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users](https://mlsanity.com/paper/2202.11134/) (2022)
- [Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations](https://mlsanity.com/paper/2110.01506/) (2021)
- [Spectrum Correction: Acoustic Scene Classification with Mismatched Recording Devices](https://mlsanity.com/paper/2105.11856/) (2021)
- [A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer](https://mlsanity.com/paper/2110.08598/) (2021)
- [Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation](https://mlsanity.com/paper/2101.00701/) (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)
- [Device Invariance using Domain Adaptation on Acoustic Scene Classification](https://mlsanity.com/paper/2607.25887/) (2026)

## Abstract (arXiv)

A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy. As a countermeasure, we present the first method of unsupervised adversarial domain adaptation for acoustic scene classification. We employ a model pre-trained on data from one set of conditions and by using data from other set of conditions, we adapt the model in order that its output cannot be used for classifying the set of conditions that input data belong to. We use a freely available dataset from the DCASE 2018 challenge Task 1, subtask B, that contains data from mismatched recording devices. We consider the scenario where the annotations are available for the data recorded from one device, but not for the rest. Our results show that with our model agnostic method we can achieve ~ 10% increase at the accuracy on an unseen and unlabeled dataset, while keeping almost the same performance on the labeled dataset.