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An Automatic Classification System for Environmental Sound in Smart Cities.

Dongping Zhang1, Ziyin Zhong1, Yuejian Xia1

  • 1Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, China Jiliang University, Hangzhou 310018, China.

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|August 12, 2023
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Summary
This summary is machine-generated.

This study introduces a dual-residual network for environmental sound classification (ESC), improving accuracy by fusing audio features. The method enhances urban noise recognition in smart city applications.

Keywords:
convolutional neural networksdata processingenvironment sound classificationresidual network

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Area of Science:

  • Computer Science
  • Signal Processing
  • Artificial Intelligence

Background:

  • Smart city initiatives increasingly integrate advanced technologies like AI and IoT.
  • Environmental sound classification (ESC) faces challenges due to non-stationary audio and urban noise interference.
  • Existing deep learning methods struggle with single-input feature extraction for accurate ESC.

Purpose of the Study:

  • To enhance environmental sound classification (ESC) accuracy in smart city contexts.
  • To address limitations in feature extraction from complex urban acoustic environments.
  • To propose an improved deep learning model for robust audio event recognition.

Main Methods:

  • A dual-residual network (dual-resnet) architecture was developed for feature fusion.
  • A loop-padding method was introduced for pre-processing shorter audio data.
  • Time-frequency data augmentation was employed to mitigate overfitting and expand the dataset.
  • Log-Mel spectrogram and log-spectrogram features were extracted and fused.

Main Results:

  • The proposed dual-resnet model demonstrated improved classification accuracy on the UrbanSound8k dataset.
  • Feature fusion from dual input branches led to more comprehensive audio representations.
  • The loop-padding and data augmentation techniques enhanced data utility and model generalization.
  • Comparative experiments showed superior performance against other existing models.

Conclusions:

  • The dual-resnet approach effectively improves environmental sound classification accuracy.
  • Feature fusion and advanced pre-processing techniques are crucial for handling noisy urban audio.
  • This method offers a promising solution for audio analysis in smart city environments.