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Environmental Sound Classification Based on CAR-Transformer Neural Network Model

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Abstract

Environment Sound Classification (ESC) has been a challenging task in the audio field due to the different types of ambient sounds involved. In this paper, we propose a method for the ESC tasks based on the CAR-Transformer neural network model, which includes stages of sound sample pre-processing, deep learning-based feature extraction, and classifier classification. We convert the one-dimensional audio signal into two-dimensional Mel Frequency Cepstral Coefficients (MFCC) and use them as the feature map of the audio. The CAR-Transformer model was used for feature extraction, and after dimensionality reduction of the extracted feature map, we use the fully connected layer as a classifier of the feature map to obtain the final results. The method achieves a classification accuracy of 96.91% on the UrbanSound8K dataset, while the number of parameters in the model is only 0.16 M. The results of this paper were compared with other state-of-art research.

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Acknowledgements

This work was supported by Hunan Key Laboratory of Intelligent Logistics Technology (2019TP1015)

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Authors and Affiliations

  1. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China

    Huaicheng Li, Aibin Chen, Jizheng Yi, Wenjie Chen, Daowu Yang & Guoxiong Zhou

  2. Hunan Zixing Artificial Intelligence Technology Group Co., Ltd, Changsha, Hunan, China

    Weixiong Peng

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  1. Huaicheng Li
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  2. Aibin Chen
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  4. Wenjie Chen
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Correspondence to Aibin Chen.

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Li, H., Chen, A., Yi, J. et al. Environmental Sound Classification Based on CAR-Transformer Neural Network Model. Circuits Syst Signal Process 42, 5289–5312 (2023). https://doi.org/10.1007/s00034-023-02339-w

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