The Study of the Effectiveness of Advanced Algorithms for Learning Neural Networks Based on FPGA in the Musical Notation Classification Task
Source abstract
Abstract
The work contains an original comparison of selected algorithms using artificial neural network models, such as RBF neural networks, and classic algorithms, approaches that are based on structured programming in the image identification task. The existing studies exploring methods for the problem of classifying musical notation used in this work are still scarce. The research of neural network based and the classical method of image recognition was carried out on the basis of the effectiveness of recognizing the notes presented on the treble staff. In order to carry out the research, the density of the data distribution was modeled by means of the probabilistic principal component analysis, and a simple regression was performed with the use of a radial neural network. The methods of image acquisition and analysis are presented. The obtained results were successively tested in terms of selected quality criteria. The development of this research may contribute to supporting the learning of musical notation by both beginners and blind people. The further development of the experiments can provide a convenient reading of the musical notation with the help of a classification system. The research is also an introduction of new algorithms to further tests and projects in the field of music notation classification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Currency Recognition and Detection · Neural Networks and Applications · Wireless Sensor Networks for Data Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Prószkowska 76 Street, 45-758 Opole, Poland
References (43)
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