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Music emotion representation based on non-negative matrix factorization algorithm and user label information.

Yuan Tian1

  • 1School of Arts, Zhengzhou Technology and Business University, Zhengzhou, Henan, China.

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|October 9, 2023
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Summary
This summary is machine-generated.

This study introduces a new music emotion model using nonnegative matrix factorization (NMF) for better emotion recognition. The novel approach significantly improves music and user emotion identification accuracy.

Keywords:
DepressionEmotional labelsMusicNMFSocial media

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Music Information Retrieval

Background:

  • Music emotion recognition is crucial for understanding user preferences and engagement.
  • Challenges include vast digital music data and limited emotion annotations.
  • Existing methods often struggle with nuanced emotional representation.

Purpose of the Study:

  • To develop a novel music emotion representation learning model.
  • To enhance user emotion recognition using derived music emotional embeddings.
  • To address data scarcity in music emotion annotation.

Main Methods:

  • Utilized nonnegative matrix factorization (NMF) to derive emotional embeddings from user listening lists and emotional labels.
  • Developed a music emotion recognition algorithm.
  • Proposed a user emotion recognition model employing similarity-weighted calculations.

Main Results:

  • The model converged within 400 iterations.
  • Achieved a 47.62% increase in F1 value across all emotion classes.
  • Demonstrated a 52.7% accuracy rate in user emotion recognition across seven emotion categories.

Conclusions:

  • The proposed NMF-based approach effectively represents music emotions.
  • The method significantly improves both music and user emotion recognition.
  • This facilitates more accurate understanding of users' emotional states through music.