Classification Algorithm of Environmental Sound Based on Residual Network
Chengxun Jiang, Erkang Li, Xinwei Yang · 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) · 2022
With the continuous development of sound classification technology, deep learning has gradually been applied to various classification tasks. In order to better recognize and classify the sound in complex environment, this paper proposes an environmental sound classification method based on residual network. After a series of data preprocessing for sound events, the Mel-frequency cepstral coefficients (MFCCs) are extracted as feature parameters and sent into the residual network to classify sound events. An UrbanSound8K dataset was evaluated for the performance of the proposed residual network model. The experimental results achieved a classification accuracy of 90.4%, which is better than that of the traditional shallow convolutional neural network.