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A Deep Learning-Based Piano Music Notation Recognition Method.

Chan Li¹

¹School of Preschool Education, Guangdong Nanhua Vocational College of Industry and Commerce, Guangzhou 510510, China.

Computational Intelligence and Neuroscience

|June 13, 2022

Summary

This summary is machine-generated.

This study introduces a computer system for piano music notation recognition and electronic synthesis. It achieves 94.4% accuracy in extracting musical information from scores, aiding music education and digital libraries.

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Area of Science:

Computer Science
Music Technology
Digital Signal Processing

Background:

Computer technology enables new approaches to music score analysis and electronic synthesis.
Image processing offers a novel perspective for understanding music score laws.
Developing effective piano music notation recognition is crucial for digital music systems.

Purpose of the Study:

To develop a system for piano music notation recognition and electronic synthesis.
To analyze piano sheet music using digital recognition and image processing techniques.
To convert recognized musical information into MIDI files for score reconstruction and transmission.

Main Methods:

Utilized the Beaulieu analysis method for system module analysis.
Employed digital recognition methods to extract piano sheet music feature matrices.
Extracted frequency points and envelope functions from digital scores for electronic synthesis.

Main Results:

Successfully extracted music information from digital piano scores.
Achieved a 94.4% correct rate in music information extraction.
Converted musical information into MIDI files, enabling score reconstruction and audio transmission.

Conclusions:

The developed system accurately recognizes piano music notation and facilitates electronic synthesis.
The system's high accuracy meets practical application needs.
This approach offers new possibilities for music digital libraries, education, and theory analysis.