An approach for Mridanga stroke transcription in Carnatic music using HGCC

Abstract

Mridanga is a percussion instrument used in Carnatic music, it is a two sided drum. Stroke is a process of striking the drum membrane leading to a unique sound. Stroke transcription is a process to identify and label different beat sounds produced by the percussion instrument in a track. It is an essential feature for music information retrieval (MIR) and auto content creation. In this paper a novel approach to Mridanga stroke transcription is proposed. Mridanga stroke transcription is similar to speech recognition in which, the approach is to use Mel-Frequency Cepstral Coefficients (MFCC) features, or variation of MFCC. To increase the classification in stroke transcription in Mridanga a new feature extraction method called Harmonic Grouping Cepstral Coefficient(HGCC) is introduced. The newly introduced method follows steps similar to MFCC during extraction the deviation lies in filters used for extraction. The proposed approach displays an accuracy of 80% for signal to noise ratio range of 10dB-40 dB a marginal gain to existing baseline MFCC. � 2019 IEEE.