Design and Development of Intelligent Music Evaluation and Learning Platform Using Hybrid Fractal Recurrent Networks

As digital technologies increasingly influence human lives, the incorporation of Web-based platforms in the music education arena has been imperative to offer interactive, accessible, and intelligent learning environments. The broad idea is to exploit Web technology to develop a network platform that facilitates real-time collaboration, personalized learning, adaptive feedback, and sharing of resources in music education. Yet, current systems are plagued by inefficiencies like low interactivity, inefficient recommendations, poor scalability, and absence of intelligent performance measures. Standard systems, being dependent on static databases, simple content delivery, and rule-based evaluations, cannot cater to the dynamic and individualistic learning needs. In this context, this research paper proposes the novel fractal Long short Term memory(LSTM) model and Deep Extreme Learning Networks for effective evaluation of different music notes from individuals. The proposed framework introduces the MelFrequency Correlation Co-efficients(MFCC) for better feature extraction process and aids for the better evaluation. The extensive experimentation has been carried out using MUSICNETS and various performance metrics were calculated. Comparative evaluation with traditional techniques shows enhanced outcomes, with an accuracy of 0.988, precision of 0.98, recall of 0.979 and F1-score of 0.986, marking the system's high resilience in recommendation, evaluation, and interaction. superiority performance guarantees scalability, real-time adaptability, and individualized user-centric learning, setting a standard for future music learning systems.