A Novel Techniques for Classification of Musical Instruments

Musical instrument classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. Signal is subjected to wavelet decomposition. A suitable wavelet is selected for decomposition. In our work for decomposition we used Wavelet Packet transform. After the wavelet decomposition, some sub band signals can be analyzed, particular band can be representing the particular characteristics of musical signal. Finally these wavelet features set were formed and then musical instrument will be classified by using suitable machine learning algorithm (classifier). In this paper, the problem of classifying of musical instruments is addressed.  We propose a new musical instrument classification method based on wavelet represents both local and global information by computing wavelet coefficients at different frequency sub bands with different resolutions. Using wavelet packet transform (WPT) along with advanced machine learning techniques, accuracy of music instrument classification has been significantly improved. Keywords: Musical instrument classification, WPT, Feature Extraction Techniques, Machine learning techniques

Image Denoising Using Digital Image Curvelet

Image reconstruction is one of the most important areas of image processing. As many scientific experiments result in datasets corrupted with noise, either because of the data acquisition process or because of environmental effects, denoising is necessary which a first pre-processing step in analyzing such datasets. There are several different approaches to denoise images. Despite similar visual effects, there are subtle differences between denoising, de-blurring, smoothing and restoration. Although the discrete wavelet transform (DWT) is a powerful tool in image processing, it has three serious disadvantages: shift sensitivity, poor directionality and lack of phase information. To overcome these disadvantages, a method is proposed which is based on Curvelet transforms which has very high degree of directional specificity. Allows the transform to provide approximate shift invariance and directionally selective filters while preserving the usual properties of perfect reconstruction and computational efficiency with good well-balanced frequency responses where as these properties are lacking in the traditional wavelet transform.Curvelet reconstructions exhibit higher perceptual quality than Wavelet based reconstructions, offering visually sharper images and in particular higher quality recovery of edges and of faint linear and curve linear features. The Curvelet reconstruction does not contain the quantity of disturbing artifacts along edges that we see in wavelet reconstruction. Digital Implementations of newly developed multiscale representation systems namely Curvelets, Ridgelet and Contourlets transforms are used for denoising the image. We apply these digital transforms to the problem of restoring an image from noisy data and compare our results with those obtained from well established methods based on the thresholding of Wavelet Coefficients. Keywords: Curvelets Transform, Discrete Wavelet Transform, Ridgelet Transform, Peak signal to Noise Ratio (PSNR), Mean Square Error (MSE)

Coding of Video Sequences Using Three Step Search Algorithm

AbstractThe rapid development in the technology has dramatic impact on the medical health care field. Medical data base obtained with latest machines like CT Machine, MRI scanner requires large amount of memory storage and also it requires large bandwidth for transmission of data in telemedicine applications. Thus there is need for video compression. As the database of medical images contain number of frames (slices), hence while coding of these images there is need of motion estimation. Motion estimation finds out movement of objects in an image sequence and gets motion vectors which represents estimated motion of object in the frame. In order to reduce temporal redundancy between successive frames of video sequence, motion compensation is preformed.In this paper three step search (TSS) block matching algorithm is implemented on different types of video sequences. It is shown that three step search algorithm produces better quality performance and less computational time compared with exhaustive full search algorithm