Comparative Study of Mfcc and Mel Spectrogram for Raga Classification Using CNN

Abstract

Objectives: To perform a comparative study of the results of feature extraction done using two different methods, Mfcc and Mel spectrogram, and determine which method is more effective for implementing the CNN algorithm. Methods: This study uses the CNN model to classify ragas according to Indian classical music. Feature extraction, which is a major operation in the Music Information Retrieval (MIR) process, is done using Mfcc and Mel spectrogram methods. The major ragas chosen as subjects for feature extraction are Yaman, Bhairav, Bhairavi, Multani, and Dhanashree. Findings: After comparison and examination of results achieved from both techniques, we could conclude that the CNN model using the Mel spectrogram method outperforms the CNN model using Mfcc. Novelty: The majority of the research, we discovered was on Carnatic music. In contrast to earlier research, this research takes a novel approach by conducting experiments on a variety of Hindustani classical ragas which are different from other studies. Researchers interested in music as well as application users would benefit from this study. Our proposed feature extraction approach will be useful for initializing the CNN algorithm, which will help aspiring musicians recognize ragas and classify songs based on these ragas. Keywords: Raga identification; Music Information Retrieval; Feature extraction; Mfcc; Melspectrograms; CNN