Development of a Methodology for Identification of Indian Musical Instruments

In this work, an attempt is made to develop a methodology for Identication of Indian Musical Instruments. Given a digital audio le with mono recording of an Indian Instrument, we identify the instrument played. The approach involves feature extraction from the signal based on Digital Signal Processing techniques. The spectral moments and pitch of the music signal are used as features. The features extracted from the training data are stored in a database for a learning system based on the k-Nearest Neighbor classier (k-NN). The k-NN method uses a priori information from the training data set to estimate posterior probabilities for an unknown data. We implement the same and test our approach for 4 Indian Instruments - Sitar, Sarod, Tabla and Bansuri. A total of 60 les consisting of 15 recordings of each of the 4 instruments were tested. The recognition was as high as 73.33% for the Tabla and as low as 26.67% for the Sitar