Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2023 - Volume 8 Issue 4, Article No: 23395
https://doi.org/10.55267/iadt.07.14016

Published Online: 26 Oct 2023

Views: 283 | Downloads: 202

The convergence of artificial intelligence (AI) and music analysis in recent years has altered how humans perceive and analyze music. The purpose of this study was to investigate the effectiveness of virtual computer systems for AI-powered music analysis, as well as how they affect musicological insights and genre classification. The goal of the project was to uncover hidden patterns inside musical compositions while improving our understanding of genre features and underlying musical structures by fusing cutting-edge AI algorithms with the possibilities of virtualization technology. A quantitative study design with controlled experiments using standardized music datasets was used. Musical compositions of various styles were chosen, and relevant musical features such as melody, rhythm, and harmony were retrieved. Metrics for performance evaluation included genre categorization accuracy, precision, recall, and F1-score, as well as efficacy indicators for musicological investigations. The findings of the study shed light on the innovative possibilities of AI-driven music analysis. Across a range of musical genres, accurate genre classification was achieved, demonstrating the accuracy of AI models in identifying subtle genre traits. Deeper knowledge of musical works was aided by the discovery of complex melodic motifs, chord progressions, and rhythmic patterns through musicological research. By highlighting the synergies between AI techniques and virtual computer systems, this study contributes to the expanding landscape of AI-powered music analysis. It demonstrates AI's potential for automating hard activities, complementing musicological investigations, and providing insights that supplement human expertise. The study demonstrated the potential of AI-powered music analysis, but it also highlighted its shortcomings due to biases in training data, model overfitting, and resource restrictions in virtual systems. These limitations highlight the necessity of constant improvement and awareness when incorporating AI into musicology.

AI-Powered Music Analysis Genre Classification Musicological Investigations Virtual Computer Systems

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