Acoustic Scene Classification (ASC) is a fundamental task in machine listening, aiming to categorize the acoustic environment in which an audio recording was made. Although Convolutional Neural Networks (CNNs) are still the dominant approach in ASC today, increasing attention has been given to optimizing model efficiency, particularly for model deployment in resource-limited computational platforms such as hearing aids. In this study, we explore Reservoir Computing (RC) as an alternative lightweight approach for ASC and systematically compare its performance against multiple CNN architectures, including ResNet-34, MobileNet-V2, EfficientNet-B2, and TF-SepNet-20. Our results indicate that CNN models consistently outperform RC in classification accuracy, even when trained without data augmentation. However, RC exhibits significantly lower computational cost, requires less training time, and achieves the fastest inference speed among all models. This trade-off between accuracy and efficiency suggests that model selection should be guided by application requirements: CNNs are preferable when accuracy is paramount, while RC provides an alternative for low-power real-time applications such as hearing aids.
(See document for exact affiliation information.)
https://aes2.org/publications/elibrary-page/?id=23010
(5441KB)
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
