Classification of Musical Instruments sounds by Using MFCC and Timbral Audio Descriptors

Abstract

Identification of the musical instrument from a music piece is becoming area of interest for researchers in recent years. The system for identification of musical instrument from monophonic audio recording is basically performs three tasks: i) Pre-processing of inputted music signal; ii) Feature extraction from the music signal; iii) Classification. There are many methods to extract the audio features from an audio recording like Mel-frequency Cepstral Coefficients (MFCC), Linear Predictive Codes (LPC), Linear Predictive Cepstral Coefficients (LPCC), Perceptual Linear Predictive Coefficients (PLP), etc. The paper presents an idea to identify musical instruments from monophonic audio recordings by extracting MFCC features and timbre related audio descriptors. Further, three classifiers K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Binary Tree Classifier (BT) are used to identify the musical instrument name by using feature vector generated in feature extraction process. The analysis is made by studying results obtained by all possible combinations of feature extraction methods and classifiers. Percentage accuracies for each combination are calculated to find out which combinations can give better musical instrument identification results. The system gives higher percentage accuracies of 90.00%, 77.00% and 75.33% for five, ten and fifteen musical instruments respectively if MFCC is used with K-NN classifier and for Timbral ADs higher percentage accuracies of 88.00%, 84.00% and 73.33% are obtained for five, ten and fifteen musical instruments respectively if BT classifier is used.
DOI: 10.17762/ijritcc2321-8169.1507130