Informatics Publishing Limited

D. G. Bhalke ¹, C. B. Rama Rao ¹, D. S. Bormane ²

Affiliations
1 N.I.T., Warangal, IN
2 R.S.C.O.E., Pune, IN

Abstract

This paper presents a study on performance of western musical instrument recognition using higher order moments, such as skewness and kurtosis. Higher-order moments contain supplementary statistical information of the signal over the conventional first or second order moments. For example skewness is used for measuring the symmetry of the distribution of data and Kurtosis is measure of flatness of data. Higher-order moments provide segment level features. These segment level features are integrating with low level features like MFCC, Spectral features, Temporal features.
Experimental result shows that low level features integrated using higher order moments improve recognition accuracy by more than 8%. Accuracy of Musical Instrument Recognition is analysed using different combination of hybrid feature set combined with higher order moment.

Keywords

Musical Instrument Recognition, Feature Extraction, Higher Order Moments.

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Sonal R. Ahirrao ¹, D. S. Bormane ²

Affiliations
1 Electronics Engineering Department, JSPM's RSCOE, Pune, IN
2 JSPM's RSCOE, Pune, IN

Abstract

Texture analysis is an important and useful area of study in machine vision. Texture provides a rich source of information about the natural scene. This paper presents Local Binary pattern (LBP) as an approach for texture analysis particular for face recognition. Face recognition has received quite a lot of attention from researchers in biometrics, pattern recognition, and computer vision communities. The idea behind using the LBP features is that the face images can be seen as composition of micro-patterns which are invariant with respect to monotonic grey scale transformations. Combining these micro-patterns, a global description of the face image is obtained. Efficiency and the simplicity of the proposed method allows for very fast feature extraction giving better accuracy than the other algorithms. The proposed method is tested and evaluated on ORL datasets combined with other university dataset to give a good recognition rate and 89% classification accuracy using LBP only and 98% when global features are combined with LBP. The experimental results show that the method is valid and feasible.

Keywords

Classification Accuracy, Face Recognition, Global Features, Local Binary Pattern, Texture Analysis.

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