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Improvement of audio feature extraction techniques in traditional Indian string musical instrument

Sundararajoo, Kohshelan (2015) Improvement of audio feature extraction techniques in traditional Indian string musical instrument. Masters thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

Audio feature extraction is an essential and significant process where audio features are extracted from the audio files whereby the extracted audio features contains relevant audio information. One of the important roles played by the audio features is to improve the classification accuracy. However, the presence of noise in the audio signals which degrades the quality of the extracted features may result in low classification accuracy. Some of the existing audio feature extraction techniques are Mel-Frequency Cepstral Coefficient (MFCC), Linear Predictive Coding (LPC), Local Discriminant Bases (LDB), Zero-Crossing Rate (ZCR) and Perceptual Linear Prediction (PLP). Furthermore, the three frequently used techniques in audio feature extraction are MFCC, LPC and ZCR. Previous research had mentioned the shortcomings of the three techniques on extracting noisy signal. This has been identified in the case of traditional Indian musical instrument where the vibration of string instrument had produced noise in the highest amplitude. Therefore, Zero Forcing Equalizer (ZFE) was proposed to equalize the noise in the highest amplitude. ZFE was integrated with three audio feature extraction techniques, namely MFCC-ZFE, LPC-ZFE and ZCR-ZFE in order to improve the performance of the existing techniques. The results show the best improvement of classification accuracies obtained for the proposed techniques of MFCC-ZFE were 98.2% of classification accuracies with 4.0% of improvement by using kNN. Meanwhile, the combined features of the MFCC-ZFE + LPC-ZFE + ZCR-ZFE have obtained 98.3% of classification accuracies with 9.1% of improvement by using kNN.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: Mrs Hasliza Hamdan
Date Deposited: 04 Apr 2016 00:37
Last Modified: 04 Apr 2016 00:37
URI: http://eprints.uthm.edu.my/id/eprint/7917
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