# Domain Mismatch Robust Acoustic Scene Classification using Channel Information Conversion

## TL;DR

This paper introduces a novel channel domain conversion method using a factorized hierarchical variational autoencoder to address device channel mismatch in acoustic scene classification, improving robustness without needing domain relationship information.

## Contribution

It proposes a new adaptation technique that does not require domain relationship data, effectively mitigating device mismatch in ASC systems.

## Key findings

- Reduces channel mismatch effects in ASC
- Improves classification robustness across different devices
- Outperforms baseline systems in experiments

## Abstract

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.