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Related Experiment Video

Updated: Oct 2, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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A Music Emotion Classification Model Based on the Improved Convolutional Neural Network.

Xiaosong Jia1,2

  • 1College of Music and Dance, JiNing Normal Unisersity, JiNing, Inner Mongolia 012000, China.

Computational Intelligence and Neuroscience
|February 24, 2022
PubMed
Summary

This study introduces a novel convolutional neural network method for music emotion recognition, achieving 92.06% accuracy in classifying four emotions by combining Mel-frequency cepstral coefficients (MFCC) and residual phase (RP) features.

Related Experiment Videos

Last Updated: Oct 2, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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Area of Science:

  • Artificial Intelligence
  • Music Information Retrieval
  • Machine Learning

Background:

  • Music emotion classification is challenging due to complex audio features.
  • Existing methods may not fully capture temporal and spectral characteristics of music.

Purpose of the Study:

  • To propose an effective music emotion recognition method using deep learning.
  • To improve the accuracy and efficiency of music emotion classification.

Main Methods:

  • Feature extraction using Mel-frequency cepstral coefficients (MFCC) and residual phase (RP).
  • Utilizing Convolutional Recurrent Neural Network (CRNN) for spectrogram analysis.
  • Employing Bidirectional Long Short-Term Memory (Bi-LSTM) for sequential feature extraction.
  • Fusing features and applying softmax with center loss for classification.

Main Results:

  • Achieved a music emotion recognition accuracy of 92.06%.
  • The loss function value was approximately 0.98.
  • Outperformed other existing methods in experimental evaluations.

Conclusions:

  • The proposed method offers a feasible approach for music emotion recognition.
  • The combination of CRNN and Bi-LSTM effectively extracts relevant audio features.
  • The method demonstrates superior performance in classifying music emotions.