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

Computational intelligence and neuroscienceยท2022
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Music Emotion Classification Method Based on Deep Learning and Improved Attention Mechanism.

Xiaoguang Jia1

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

Computational Intelligence and Neuroscience
|June 30, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel music emotion classification method using deep learning and an improved attention mechanism. The approach enhances accuracy by analyzing both audio and lyrics, overcoming limitations of single-modal methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Music Information Retrieval

Background:

  • Existing music emotion classification methods often rely on single-modal analysis (audio or lyrics), leading to information loss.
  • The correlation between different data modalities in music emotion analysis is frequently overlooked.

Purpose of the Study:

  • To propose a novel music emotion classification method leveraging deep learning and an improved attention mechanism.
  • To address the information loss issue in single-modal music emotion analysis.

Main Methods:

  • Feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec for lyrics.
  • Construction of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network model with an integrated attention mechanism.
  • Fusion of deep neural network outputs with the CNN-LSTM model, followed by classification using a Softmax classifier.

Main Results:

  • The proposed method achieved an average classification accuracy of 0.848 on selected datasets.
  • Demonstrated superior performance compared to existing music emotion classification methods.
  • Significantly improved classification efficiency.

Conclusions:

  • The developed deep learning model with an improved attention mechanism effectively classifies music emotions.
  • Multi-modal analysis integrating lyrics and audio features enhances classification accuracy and efficiency.
  • The proposed method offers a promising advancement in music emotion recognition research.