Lightweight Convolutional Recurrent Neural Networks for Sound Event Classification
DOI:
https://doi.org/10.52731/liir.v006.355Keywords:
Sound Event Classification, Convolutional Neural Network, Convolutional Recurrent Neural Network, TransformerAbstract
Sound Event Classification (SEC) is essential for applications like urban noise monitoring and smart home automation, but modern models often struggle with efficiency and deployability. This study evaluated lightweight SEC architectures namely CNN, CRNN, and Transformer using the UrbanSound8K dataset, considering both accuracy and resource consumption. CRNN emerged as the top performer, achieving around 90% accuracy with only 175,754 parameters, surpassing the efficiency of CNNs and Transformers. These results underscore the CRNN's potential for scalable and cost-effective SEC solutions, making it ideal for smart city infrastructure and resource-limited IoT applications.
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