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A Music Playback Algorithm Based on Residual-Inception Blocks for Music Emotion Classification and Physiological

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  • 1Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.

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Summary
This summary is machine-generated.

This study introduces an AI-powered music system for runners, synchronizing music emotion with physiological signals to enhance performance and motivation during exercise. The system is energy-efficient and suitable for mobile devices.

Keywords:
convolutional neural networksdeep learningemotion classificationmusic selection modulephysiological data

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Area of Science:

  • Exercise Physiology
  • Computational Intelligence
  • Music Information Retrieval

Background:

  • Music is known to enhance athletic performance and motivation.
  • Practical applications of music interventions in exercise are underrepresented in research.
  • Existing systems lack integration of real-time physiological data and emotional music analysis.

Purpose of the Study:

  • To design an energy-efficient music playback system for joggers.
  • To integrate artificial intelligence (AI) with physiological signals and emotional music for personalized selection.
  • To improve the practical implementation of music during exercise for enhanced user efficiency.

Main Methods:

  • Developed a music selection module using AI techniques, physiological data, and emotional music analysis.
  • Optimized the model using logarithm scaled Mel-spectrograms for reduced computational complexity (FLOPs) and parameters.
  • Evaluated model performance on Bi-modal, 4Q emotion, and Soundtrack datasets.

Main Results:

  • The proposed model achieved competitive accuracy: 84.91% (Bi-modal), 92.04% (4Q emotion), and 87.24% (Soundtrack).
  • Demonstrated significant reductions in computational complexity and inference time compared to existing models.
  • The model's small size allows application on mobile devices with limited resources.

Conclusions:

  • The designed playback system effectively links music emotion with physiological states during exercise.
  • The system offers a practical solution to improve users' exercise efficiency and motivation.
  • The AI-driven, low-power approach is suitable for real-time application on portable devices.