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Lightweight deep convolutional neural network for background sound classification in speech signals.

Aveen Dayal1, Sreenivasa Reddy Yeduri1, Balu Harshavardan Koduru1

  • 1Department of ICT, University of Agder, Grimstad 4879, Norway.

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This study introduces a lightweight deep convolutional neural network (CNN) for classifying background sounds within human speech. The model achieves high accuracy, outperforming others on edge devices.

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Area of Science:

  • Speech Signal Processing
  • Machine Learning
  • Audio Analysis

Background:

  • Background sound classification is crucial for many applications.
  • Previous research has not focused on sounds embedded within real-world human speech.
  • Distinguishing background noise from speech is a significant challenge.

Purpose of the Study:

  • To propose an efficient method for background sound classification in human speech signals.
  • To develop a lightweight deep convolutional neural network (CNN) model for this task.
  • To evaluate the model's performance on practical, real-world speech data.

Main Methods:

  • Utilized spectrograms as input features for the deep convolutional neural network (CNN).
  • Developed a lightweight CNN architecture with four convolution, four max-pooling, and one fully connected layer.
  • Tested the model on human speech signals with diverse signal-to-noise ratios (SNRs).

Main Results:

  • Achieved an overall background sound classification accuracy of 95.2% across various SNRs.
  • The model successfully classified 11 distinct background sound categories embedded in speech.
  • Demonstrated superior performance compared to benchmark models in accuracy and inference time.

Conclusions:

  • The proposed lightweight deep CNN model with spectrograms is highly effective for background sound classification in human speech.
  • The model offers an efficient solution suitable for edge computing applications.
  • This approach addresses a gap in research by focusing on practical, embedded background sounds.