No abstract

The concept of Music Therapy in India is approached with different terms like Music Therapy or Healing Music, Nada Chikitsa, Nada Yoga, Raga Chikitsa, and Raga Therapy. Out of many languages in the world, which are used to convey information, the language that everyone can understand is Music. All Humans can be influenced by music. Music is one mode of communication that is pleasant to listen and it heals many mental diseases. These powers of healing of Music are rediscovered in modern medicine using modern science. In recent times, treating persons with physical and mental health issues using music therapy is getting more significant. The main objective of this paper is to implement and evaluate the performance of raga identification using a machine learning algorithm and to compare the accuracy of the raga identification system which uses Mel Frequency Cepstral Coefficients (MFCC) as a feature and the system which uses pitch and chroma information along with MFCC features. A machine learning-based algorithm is proposed to identify the Raga recognition for music therapy. This method uses MFCC (Mel Frequency Cepstral Coefficients) feature along with Pitch and Chroma information for feature extraction. KNN method employed for classification of ragas. The proposed approach varies from existing methods where the notes of temporal information were ignored in the pitch-class profile method. The dataset has been collected from Kaggle to evaluate the proposed method. The proposed classification system is designed to identify different ragas (Asvari, Bageshree, Bhairavi, Darbari, and Yaman) in an open-set approach. The performance of the proposed classifier is observed to be 92.34%.