Recommendation System for Teaching Vocal Music for College students based on Bi-directional Long Short-Term Memory with Capsule Networks

In today’s generation, people are interested to learn and explore numerous types of skills to enjoy their life such as music, dance, art, photography etc. From the research, it is analyzed most of the college students are interested and passionate about the vocal music however evaluating vocal music performance is a challenging task due to the complex and subjective nature of music which requires several parameters to compute such as pitch, tone quality, rhythm, presentation and modulation. Many previous researchers have suggested various conventional methods but still could not resolve and accurately recommend better system to evaluate the vocal music. So, to develop a better recommendation system, this paper suggests an effective approach namely Bi-LSTM-CapsNet for development of College vocal music teaching system using Fuzzy evaluation and Bi-directional Long Short-Term Memory (Bi-LSTM) with Capsule Networks (CapsNet). Here, fuzzy evaluation is employed to model fuzzy logic and rules that assist to handle and model the ambiguities in-built in musical assessment. To further fine-tune this process, a recommendation system is suggested where Bi-LSTM detects sequential patterns of the college students then, CapsNet recognizes temporal and spatial features, enhancing the model’s ability to interpret complex aspects of vocal quality. So by combing these algorithms enhances the ability to evaluate the vocal music among college students.