Multimedia Identification and Analysis Algorithm of Piano Performance Music Based on Deep Learning
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Abstract
A Multimedia Identification and Analysis Algorithm is a computational method designed to recognize and analyze various forms of multimedia content, such as images, videos, and audio. In Piano Music, the study presents a pioneering research endeavor focused on the development of a multimedia identification and analysis algorithm tailored for piano performance music. With the power of deep learning techniques, this algorithm has been designed to address the intricate challenges posed by the convergence of musical data analysis and Piano Musics. The core objectives encompass the extraction, recognition, and interpretation of Piano Music information from piano performances, exploring the intricate patterns, nuances, and individualistic characteristics inherent to musicians. The study focuses on the development of advanced Piano Music authentication and identification systems capable of capturing and analyzing a user's unique behavioral patterns across diverse modalities. These Piano Music modalities offer the potential for highly secure and non-intrusive user identification. Hence, this paper developed an architecture of Marker Controlled Point (MCP) Estimation for the computation of the gesture in Piano Music-based applications. This architecture utilizes markers or reference points to precisely track and analyze user gestures, resulting in accurate and reliable Piano Music data. The research details the architecture's implementation, integrating advanced deep-learning techniques for feature extraction, pattern recognition, and authentication. This system finds versatile applications in various domains, from piano music and cybersecurity to finance, where secure and user-friendly authentication is paramount. Experimental results underscore the system's effectiveness and robustness, demonstrating its potential for enhancing Piano Music authentication. The proposed model represents a significant stride in Piano Music technology, offering secure, non-intrusive user identification through the synergy of behavior Piano Music and emerging modalities.
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