# Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching

arXiv: https://arxiv.org/abs/2005.00145 · HTML: https://mlsanity.com/paper/2005.00145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices](https://mlsanity.com/paper/2110.09598/) (2021)
- [Visual and audio scene classification for detecting discrepancies in video: a baseline method and experimental protocol](https://mlsanity.com/paper/2405.00384/) (2024)

## Abstract (arXiv)

The performance of machine learning algorithms is known to be negatively affected by possible mismatches between training (source) and test (target) data distributions. In fact, this problem emerges whenever an acoustic scene classification system which has been trained on data recorded by a given device is applied to samples acquired under different acoustic conditions or captured by mismatched recording devices. To address this issue, we propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset. This model-agnostic approach is devised to adapt audio samples from unseen devices before they are fed to a pre-trained classifier, thus avoiding any further learning phase. Using the DCASE 2018 Task 1-B development dataset, we show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.