Acoustic Scene Classification Across Multiple Devices Through Incremental Learning of Device-Specific Domains

In this paper, we propose using a domain-incremental learning approach for coping with different devices in acoustic scene classification. While the typical way to handle mismatched training data is through domain adaptation or specific regularization techniques, incremental learning offers a different approach. With this technique, it is possible to learn the characteristics of new devices on-the-go, adding to a previously trained model. This also means that new device data can be introduced at any time, without a need to retrain the original model. In terms of incremental learning, we propose a combination of domain-specific Low-Rank Adaptation (LoRA) parameters and running statistics of Batch Normalization (BN) layers. LoRA adds low-rank decomposition matrices to a convolutional layer with a few trainable parameters for each new device, while domain-specific BN is used to boost performance. Experiments are conducted on the TAU Urban Acoustic Scenes 2020 Mobile development dataset, containing 9 different devices; we train the system using the 40h of data available for the main device, and incrementally learn the domains of the other 8 devices based on 3h of data available for each. We show that the proposed approach outperforms other fine-tuning-based methods, and is outperformed only by joint learning with all data from all devices.