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This study introduces an improved computer-aided piano music automatic notation algorithm using variable Q-transform and convolutional neural networks (CNNs). This advanced algorithm enhances music coherence and aids in psychological detoxification.

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Area of Science:

  • Music Information Retrieval
  • Computational Musicology
  • Signal Processing

Background:

  • Traditional Constant Q-transform (CQT) offers high frequency resolution but limited temporal resolution in music analysis.
  • Convolutional Neural Networks (CNNs) are increasingly used for music transcription tasks, but require optimization for accuracy and efficiency.
  • Maintaining musical coherence and inter-track dependencies is crucial for automated music generation and notation.

Purpose of the Study:

  • To develop and optimize a computer-aided piano music automatic notation algorithm.
  • To improve time-frequency analysis for music signals using variable Q-transform.
  • To enhance music generation coherence and explore its role in psychological detoxification.

Main Methods:

  • Investigated and implemented variable Q-transform for enhanced time-frequency representation of music signals.
  • Utilized CNN models for note onset and multibasic tone detection, optimizing network structure, training, and postprocessing.
  • Developed a temporal structure model for music coherence and a multi-channel method for generating discrete music events.

Main Results:

  • Variable Q-transform demonstrated superior temporal resolution compared to CQT for music signal analysis.
  • Optimized CNN models achieved effective note onset and tone detection.
  • A novel temporal structure model and multi-channel generation method were successfully implemented for coherent music creation.

Conclusions:

  • The proposed automatic piano music notation algorithm, leveraging variable Q-transform and optimized CNNs, offers significant improvements in music transcription and generation.
  • The algorithm's ability to maintain music coherence and its potential application in psychological detoxification warrant further investigation.
  • This work contributes to advancing computational musicology and its therapeutic applications.