Multimedia Identification and Analysis Algorithm of Piano Performance Music Based on Deep Learning

Abstract

In the biometric biomedical applications, 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 biometrics. The core objectives encompass the extraction, recognition, and interpretation of biometric information from piano performances, exploring the intricate patterns, nuances, and individualistic characteristics inherent to musicians. The study focuses on the development of advanced biometric authentication and identification systems capable of capturing and analyzing a user's unique behavioral patterns across diverse modalities. These biometric 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 biometric-based applications. This architecture utilizes markers or reference points to precisely track and analyze user gestures, resulting in accurate and reliable biometric 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 healthcare 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 biometric authentication. The proposed model represents a significant stride in biometric technology, offering secure, non-intrusive user identification through the synergy of behavior biometrics and emerging modalities.