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Algorithm Composition and Emotion Recognition Based on Machine Learning.

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This study introduces a machine learning algorithm for music emotion recognition, achieving 93.78% accuracy. The method segments music and analyzes features to classify emotions, aiding composers and intelligent music services.

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Area of Science:

  • Machine Learning
  • Music Information Retrieval
  • Affective Computing

Background:

  • Music emotion recognition is challenging due to subjective interpretations.
  • Existing methods often lack robust feature extraction and accurate classification.
  • Developing automated systems for music emotion analysis is crucial for various applications.

Purpose of the Study:

  • To propose a novel machine learning algorithm for music emotion recognition.
  • To develop a model for recognizing musical emotions by analyzing music characteristics.
  • To quantify music features and emotions for improved classification accuracy.

Main Methods:

  • Developed an algorithm composition network based on machine learning.
  • Extracted main melody tracks using information entropy of pitch and intensity.
  • Utilized cosine of vector included angle for segment similarity and music segmentation.
  • Employed a music emotion model to analyze segmented music features.
  • Quantified music features and mapped them to emotions for classification.

Main Results:

  • Achieved a music emotion recognition accuracy of 93.78%.
  • Obtained an algorithm recall rate of 96.3%.
  • Demonstrated superior recognition ability and reliability compared to other methods.
  • Successfully segmented music into independent sections for emotion analysis.

Conclusions:

  • The proposed model accurately identifies music emotions by quantifying music features.
  • The algorithm composition network provides a reliable method for music emotion recognition.
  • This research supports auxiliary creative needs for composers and enhances intelligent music services.