Vocal music teaching method using fuzzy logic approach for musical performance evaluation

Abstract

Vocal music teaching through E-learning platforms and in-person classrooms requires intense attention and practice. The vocals and strings with/without instrument support are required for improving the voice, pitch presentation, and learning improvement. With digitalization, the assessments are performed using a computer and process-aided technologies for musical performance evaluation. This article introduces a Leveled-Fuzzy Logic Approach (LFLA) for evaluating the musical performance of the vocal teaching method. The evaluation improves the teaching mode by matching the actual learning guidelines. The vocal music teaching guidelines vary for different musical sessions and types. Such different aspects are analyzed using leveled fuzzy; the session flow is analyzed for maximum inference with the actual learning process. The number of learning levels forms the crisp input and the input is analyzed until the fuzzification extracts precise high matching for the guided and real-time teaching. The crisp inputs are induced for combinational inference during the fuzzification process using lagging features. The lagging features such as time, sessions, and evaluation per session are considered for fuzzification. The proposed approach is verified using the metrics assessment rate, lag detection, assessment time, and errors.