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BSN-ESC: A Big-Small Network-Based Environmental Sound Classification Method for AIoT Applications.

Lujie Peng1, Junyu Yang1, Longke Yan1

  • 1Department of Internet of Things Engineering, School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary

A new BSN-ESC model significantly reduces computational complexity for environmental sound classification (ESC). This makes AIoT devices more efficient while maintaining high accuracy, enabling wider application of sound analysis.

Keywords:
AIoTenvironmental sound classificationlow computational complexityneural network

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Area of Science:

  • Artificial Intelligence
  • Internet of Things (AIoT)
  • Signal Processing

Background:

  • Environmental sound classification (ESC) is crucial for AIoT applications.
  • Existing ESC methods are computationally intensive, limiting AIoT deployment.
  • There's a need for efficient ESC models for resource-constrained devices.

Purpose of the Study:

  • To develop a novel ESC method (BSN-ESC) with high accuracy and low computational complexity.
  • To enable the deployment of ESC on AIoT devices with limited resources.

Main Methods:

  • Proposed a big-small network (BSN)-based ESC model that adaptively selects network size based on classification difficulty.
  • Introduced a pre-classification technique using log-mel spectrogram refining to prevent frequency distortion.
  • Implemented and evaluated the BSN-ESC model on CPU and FPGA using the ESC-50 dataset.

Main Results:

  • Achieved significantly reduced computational complexity (0.123G FLOPs), a 2309x improvement over state-of-the-art.
  • Maintained high classification accuracy of 89.25% on the ESC-50 dataset.
  • Demonstrated effective performance on both PC and embedded systems.

Conclusions:

  • The BSN-ESC model offers a viable solution for efficient ESC on resource-constrained AIoT devices.
  • The proposed methods successfully balance computational efficiency and classification performance.
  • This work facilitates the integration of advanced sound analysis into edge computing applications.