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Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

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Abstract

Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works’ effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN) to overcome these problems. The PIPMN reaches 95.5% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or transfer learning. The PIPMN can achieve similar or even exceeds other state-of-the-art models with much less parameters under this setting. The Code is available on the https://github.com/JNAIC/PIPMN.

Y. Chen, Y. Zhu and Z. Yan—contributed equally to the paper.

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Author information

Authors and Affiliations

  1. Jiangnan University, Wuxi, 214000, China

    Yunhao Chen, Zihui Yan, Zhen Ren, Yifan Huang, Jianlu Shen & Lifang Chen

  2. University of Leeds, Leeds, LS2 9JT, UK

    Yunjie Zhu

Authors
  1. Yunhao Chen
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  2. Yunjie Zhu
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  3. Zihui Yan
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  4. Zhen Ren
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  5. Yifan Huang
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  6. Jianlu Shen
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  7. Lifang Chen
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Corresponding author

Correspondence to Yunhao Chen .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Chen, Y. et al. (2023). Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_6

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  • DOI: https://doi.org/10.1007/978-981-99-4742-3_6

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  • Online ISBN: 978-981-99-4742-3

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