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Recognition of Continuous Music Segments Based on the Phase Space Reconstruction Method
1School of Music and Dance, Quanzhou Normal University, Quanzhou, Fujian 362000, China.
Computational Intelligence and Neuroscience
|October 14, 2022
Summary
This study introduces an improved piano score recognition method using adaptive signal decomposition and support vector machines (SVM). The novel approach enhances accuracy by modeling harmonic and vocal information, achieving a 3.5% to 12.2% improvement over existing algorithms.
Area of Science:
- Music Information Retrieval
- Signal Processing
- Machine Learning
Background:
- Piano score recognition is crucial for music information retrieval.
- Vocal interference and harmonic restoration are key challenges in piano note recognition.
Purpose of the Study:
- To develop an advanced piano score recognition algorithm.
- To reduce vocal influence and accurately restore harmonic information.
- To improve the accuracy of piano note recognition in continuous music.
Main Methods:
- Modeling harmonic and vocal information in the frequency spectrum.
- Utilizing phase space reconstruction for nonlinear feature extraction.
- Employing adaptive signal decomposition and Support Vector Machines (SVM) with measurement learning for classification.
- Comparing SVM with adaptive signal decomposition and principal component analysis preprocessing.
Main Results:
- The proposed method achieved higher accuracy compared to current popular algorithms.
- Recognition accuracy improved by 3.5% to 12.2%.
- Direct phase space reconstruction of the original signal demonstrated increased accuracy.
Conclusions:
- The developed method effectively reduces vocal interference and enhances piano score recognition.
- The integration of adaptive signal decomposition, SVM, and phase space reconstruction offers a robust approach.
- The algorithm is suitable for recognizing notes in continuous musical pieces.
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