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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P. Muralidhar ¹, C. B. Rama Rao ¹

Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, IN

Abstract

In Scalable Video Coding (SVC), motion estimation and inter-layer prediction play an important role in elimination of temporal and spatial redundancies between consecutive layers. This paper evaluates the performance of widely accepted block matching algorithms used in various video compression standards, with emphasis on the performance of the algorithms for a didactic scalable video codec. Many different implementations of Fast Motion Estimation Algorithms have been proposed to reduce motion estimation complexity. The block matching algorithms have been analyzed with emphasis on Peak Signal to Noise Ratio (PSNR) and computations using MATLAB. In addition to the above comparisons, a survey has been done on Spiral Search Motion Estimation Algorithms for Video Coding. A New Modified Spiral Search (NMSS) motion estimation algorithm has been proposed with lower computational complexity. The proposed algorithm achieves 72% reduction in computation with a minimal (<1dB) reduction in PSNR. A brief introduction to the entire flow of video compression H.264/SVC is also presented in this paper.

Keywords

Scalable Video Coding (SVC), Sum of Absolute Difference (SAD), Blocking Matching, Motion Estimation, Inter-Layer Prediction, Peak Signal to Noise Ratio (PSNR), Spiral Search, H.264/SVC.

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