Talk affirmation is one among the basic zones in cutting edge talk process. The examination of talk affirmation may be a bit of an examination for "artificial intelligence" machines that may "hear" and "appreciate" the verbally communicated data. The customary ways for talk affirmation like HMM and DTW, are outrageously inconvenient and time excellent. As such formal Fuzzy justification may be an endeavor in cutting edge talk process for the convincing portrayal of talk affirmation in a couple of utilization. The approach masterminded in the midst of this paper streamlines the utilization of fuzzy in talk affirmation and make the data dealing with time shorter. The case considered in the midst of this paper is that the least mind boggling, i.e., the example of speaker dependence, little vocabulary and disconnected words. There are various spectral and common choices isolated from human talk. The present ways for tendency acknowledgment from voice use basically MFCC and Energy feature. This paper briefs an overview concerning the present work on talk feeling ID strong for completing more examination by feathery approach.
INTRODUCTION:In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF).OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc.
METHODS, RESULTS AND CONCLUSION:The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels.
This paper discuss about new AM-FM methods for image reconstruction. This approach is based on 2 basic ideas: i) AM-FM Demodulation using new gabor filterbank ii) New accurate methods for instantaneous frequency (IF) estimation. This project includes quasi-eigen function approximation(QEA), quasi local method(QLM) and variable-spacing local linear phase (VS-LLP) methods for improved accuracy. The new VS-
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