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Labeling Emotion01:20

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
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Related Experiment Video

Updated: Oct 8, 2025

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Music Composition and Emotion Recognition Using Big Data Technology and Neural Network Algorithm.

Yu Wang1

  • 1Zhoukou Vocational and Technical College, Henan, Zhoukou 466002, China.

Computational Intelligence and Neuroscience
|December 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Music Composition Neural Network (MCNN) for intelligent music generation, enhancing accuracy and enabling emotion recognition in compositions. The model facilitates sophisticated music creation for Chinese users.

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Area of Science:

  • Artificial Intelligence
  • Computational Musicology
  • Machine Learning

Background:

  • Current music composition models lack maturity for specific user bases like Chinese audiences.
  • Analyzing music composition and emotion recognition requires advanced computational techniques.

Purpose of the Study:

  • To develop an intelligent music composition model tailored for Chinese users.
  • To integrate emotion recognition capabilities into AI-generated music.
  • To enhance the accuracy and style control of music generation.

Main Methods:

  • Utilized big data technology and Neural Network (NN) algorithms.
  • Proposed a novel Music Composition Neural Network (MCNN) structure.
  • Employed Long Short-Term Memory (LSTM) networks with a constructed Reward function.
  • Incorporated music theory rules for style restriction.
  • Performed time-frequency, frequency, nonlinearity, and time-domain analyses.
  • Implemented emotion feature recognition and extraction.

Main Results:

  • Increased iteration times improved model accuracy and learning ability.
  • Higher iteration counts led to a slow decrease in the loss function.
  • Generated music exhibited emotions such as sadness, joy, loneliness, and relaxation.
  • The MCNN model demonstrated effective intelligent music composition and emotion analysis.

Conclusions:

  • The proposed MCNN model advances music composition intellectualization.
  • This research impacts traditional music composition methods.
  • The model successfully generates music with specific emotional characteristics.