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

Domain Mismatch Robust Acoustic Scene Classification using Channel Information Conversion