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Attention Based Convolutional Neural Network with Multi-frequency Resolution Feature for Environment Sound

Minze Li1, Wu Huang2, Tao Zhang1

  • 1Chengdu Techman Sofeware Co., Ltd., Chengdu, Sichuan China.

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|October 31, 2022
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Summary
This summary is machine-generated.

This study introduces a novel multi-frequency resolution (MFR) feature for environmental sound classification, improving accuracy by effectively capturing diverse sound characteristics. The proposed SacNet model achieves state-of-the-art results on benchmark datasets.

Keywords:
Convolutional neural networkDepthwise separable convolutionEnvironment sound classificationMulti-frequency resolutionSpatial attention moduleTime–frequency feature

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Area of Science:

  • Acoustics and Signal Processing
  • Machine Learning for Audio Analysis

Background:

  • Environmental sound classification is crucial for intelligent audio monitoring.
  • Existing time-frequency features struggle to represent the complexity of diverse sound types effectively.
  • A need exists for enhanced feature extraction methods to improve sound classification accuracy.

Purpose of the Study:

  • To propose a novel multi-frequency resolution (MFR) feature for environmental sound classification.
  • To develop an effective neural network architecture (SacNet) for processing these enhanced features.
  • To improve the accuracy and robustness of environmental sound classification systems.

Main Methods:

  • A novel multi-frequency resolution (MFR) feature is proposed, combining Log-Mel Spectrogram, Cochleagram, and Constant Q-Transform with varying time-dimension compression.
  • The MFR feature acts as a data augmentation technique and captures richer contextual information.
  • A new network, SacNet, utilizing depthwise separable convolution and spatial attention modules, is designed to process time-frequency features efficiently.

Main Results:

  • The proposed MFR features and SacNet achieved state-of-the-art accuracy on ESC10 (97.5%), ESC50 (93.1%), and UrbanSound8K (95.3%) datasets.
  • Performance improvements over previous advanced methods were 3.3%, 0.5%, and 2.3% respectively.
  • The method demonstrated enhanced ability to extract relevant information from time-frequency feature maps.

Conclusions:

  • The proposed multi-frequency resolution (MFR) feature effectively addresses limitations of single-resolution features in environmental sound classification.
  • SacNet provides an efficient architecture for processing complex audio features, leading to significant accuracy gains.
  • This research advances the field of intelligent audio monitoring through improved sound classification techniques.