Improvement of audio feature extraction techniques in traditional indian musical instrument

Abstract

Traditional Indian musical instrument is one of the oldest musical instruments in the world. The musical instruments have their own importance in the field of music. Traditional Indian musical instrument could be categorized into three types such as stringed instruments, percussion instruments and windblown instruments. However, this paper will focus on string instruments because its show fluctuating behavior due to noise. Therefore, three techniques are selected based on the frequently used by previous researches which show some shortcoming while extracting noisy signal. The three techniques are Mel-Frequency Cepstral Coefficient (MFCC), Linear Predictive Coding (LPC) and Zero-Crossing Rate (ZCR). Hence, this research attempts to improve the feature extracting techniques by integrating Zero Forcing Equalizer (ZFE) with those extraction techniques. Three classifiers that are k-Nearest Neighbor (kNN), Bayesian Network (BNs) and Support Vector Machine (SVM) are used to evaluate the performance of audio classification accuracy. The proposed technique shows better classification accuracy when dealing with noisy signal.