Musical instrument identification using MFCC
Monica S. Nagawade, Varsha R. Ratnaparkhe · 2017
Music signal processing is one of the active research area now a days. Identifying the musical instrument form the solo recordings is one of the applications under signal processing. Different types of algorithms have been proposed till now to identify the instrument. For this objective first of all we have to extract the features from sound samples and then we can use them for instrument identification. In this paper we are presenting one of efficient method to extract the features from sound sample that is Mel Frequency Cepsrtal Coefficient (MFCC). These MFCCs are closely related to behavior of human auditory system. While calculating these MFCCs we actually deal with spectral envelop. And hence it gives very distinctive features of the sound samples. So using these features for identification of musical instrument we can increase the accuracy of identification. In our work we have used five instruments and 30 samples of each instrument. In the training database total 90 samples (60% of total samples) and for testing database 60 (40%)samples are used. In the classification phase we have used K-Nearest Neighbor (K-NN) classifier. Proposed system gives the accuracy of 91.66% for Cello, Piano and Trumpet and accuracy of 83.33% for Flute and Violin.