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Music Emotion Classification Method Based on Deep Learning and Improved Attention Mechanism.

Computational intelligence and neuroscienceยท2022
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Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network.

Xiaoguang Jia1

  • 1School of Music, Baotou Teachers' College, lnner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014030, China.

Computational Intelligence and Neuroscience
|July 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an explicit sparse attention network combined with deep learning for accurate music emotion recognition. The method enhances classification accuracy for complex music datasets, achieving notable results for happy and sad emotions.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Music Information Retrieval

Background:

  • Accurate music emotion recognition is challenging due to the complexity of musical data.
  • Existing deep learning models may struggle with irrelevant information, impacting classification accuracy.

Purpose of the Study:

  • To enhance music emotion recognition and classification accuracy using a novel deep learning approach.
  • To improve the ability to classify emotions in complex music datasets.

Main Methods:

  • Preprocessing music data using fine-grained segmentation for high-quality input.
  • Integrating an explicit sparse attention network into a deep learning framework.
  • Reducing the impact of irrelevant information on emotion recognition.

Main Results:

  • Achieved a recognition accuracy of 0.71 for happy emotions.
  • Achieved a recognition accuracy of 0.688 for sad emotions.
  • Demonstrated improved music emotion recognition and classification capabilities.

Conclusions:

  • The proposed method effectively improves music emotion recognition accuracy.
  • The explicit sparse attention network is beneficial for handling complex music datasets.
  • The approach shows promise for practical applications in music emotion analysis.