Traditional psychological awareness relating to vocal musical instruction often disregards the impact of earlier experiences on music learning could result in a gap in meeting the needs of individual students. Conventional learning techniques of music related to psychological awareness for each individual has been focused on and addressed in this research. Technological upgrades in Fuzzy Logic (FL) and Big Data (BD) related to Artificial Intelligence (AI) are provided as a solution for the existing challenges and provide enhancement in personalized music education. The combined approach of BD-assisted Radial Basis Function is added with the Takagi Sugeno (RBF-TS) inference system, able to give personalized vocal music instruction recommendations and indulge psychological awareness among students. Applying Mel-Frequency Cepstral Coefficients (MFCC) is beneficial in capturing variant vocal characteristics as a feature extraction technique. The BD-assisted RBF can identify the accuracy of pitch differences and quality of tone, understand choices from students, and stimulate psychological awareness. The uncertainties are addressed by using the TS fuzzy inference system and delivering personalized vocal training depending on different student preference factors. With the use of multimodal data, the proposed RBF-TS approach can establish a fuzzy rule base in accordance with the personalized emotional elements, enhancing self-awareness and psychological well-being. Validation of the proposed approach using an Instruction Resource Utilization Rate (IRUR) gives significant improvements in engaging students, analyzing the pitching accuracy, frequency distribution of vocal music instruction, and loss function called Mean Square Error(MSE). The proposed research algorithm pioneers a novel solution using advanced AI algorithms addressing the research challenges in existing personalized vocal music education. It promises better student outcomes in the field of music education.
