# Domain Mismatch Robust Acoustic Scene Classification using Channel Information Conversion

arXiv: https://arxiv.org/abs/1812.01731 · HTML: https://mlsanity.com/paper/1812.01731/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech Recognition](https://mlsanity.com/paper/2409.12386/) (2024)
- [Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices](https://mlsanity.com/paper/2110.09598/) (2021)
- [Macro-block dropout for improved regularization in training end-to-end speech recognition models](https://mlsanity.com/paper/2212.14149/) (2022)
- [Universal Robust Speech Adaptation for Cross-Domain Speech Recognition and Enhancement](https://mlsanity.com/paper/2602.04307/) (2026)

## Abstract (arXiv)

In a recent acoustic scene classification (ASC) research field, training and test device channel mismatch have become an issue for the real world implementation. To address the issue, this paper proposes a channel domain conversion using factorized hierarchical variational autoencoder. Proposed method adapts both the source and target domain to a pre-defined specific domain. Unlike the conventional approach, the relationship between the target and source domain and information of each domain are not required in the adaptation process. Based on the experimental results using the IEEE detection and classification of acoustic scenes and event 2018 task 1-B dataset and the baseline system, it is shown that the proposed approach can mitigate the channel mismatching issue of different recording devices.