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Environmental sound classification using temporal-frequency attention based convolutional neural network.

Wenjie Mu1, Bo Yin2,3, Xianqing Huang4

  • 1College of Information Science and Engineering, Ocean University of China, Qingdao, China.

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|November 4, 2021
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Summary
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This study introduces a novel temporal-frequency attention convolutional neural network (TFCNN) for environmental sound classification. The model effectively captures critical time-frequency features, improving accuracy by focusing on relevant audio information.

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Area of Science:

  • Audio Recognition
  • Machine Learning
  • Signal Processing

Background:

  • Environmental sound classification is crucial in audio recognition.
  • Environmental sounds possess complex time-frequency structures compared to speech or music.
  • Existing methods struggle to effectively learn time-frequency features from Log-Mel spectrograms.

Purpose of the Study:

  • To propose an effective method for learning time-frequency features from Log-Mel spectrograms for environmental sound classification.
  • To introduce a temporal-frequency attention based convolutional neural network (TFCNN) model.
  • To enhance the representation ability of network models for improved classification accuracy.

Main Methods:

  • Designed an experiment to validate the impact of specific frequency bands on classification.
  • Developed novel temporal attention and frequency attention mechanisms.
  • Combined attention mechanisms for feature information complementarity to capture critical time-frequency features.

Main Results:

  • The proposed attention mechanisms effectively reduce the influence of background noise and irrelevant frequency bands.
  • Feature information complementarity enhances the capture of critical time-frequency features.
  • Experiments on UrbanSound 8K and ESC-50 datasets demonstrated the effectiveness of the TFCNN model.

Conclusions:

  • The proposed temporal-frequency attention based convolutional neural network (TFCNN) model significantly improves environmental sound classification.
  • The novel attention mechanisms enhance the model's ability to focus on relevant time-frequency features.
  • The method offers a promising approach for complex audio recognition tasks.