New-Folder-(4).rar - Low bit-rate speech coders are important in many critical communication systems such as military, police, fire departments, homeland security and other first responders. The need for elimination, reduction of the redundancy or irrelevant information from the analog signals gave birth to an area of speech coding. The main goal of speech coding is to represent speech with the minimum number of bits by maintaining its intelligibility and perceptual quality. In today’s wireless communication system there are many factors to be considered but the most constraint is the bandwidth and power. Accommodating more users within a limited bandwidth is a big challenge in the mobile communication system. Still the manufactures and service providers are in search for the low bit-rate speech coders that deliver toll quality speech. The objective of this paper is to compare the commonly used algorithms in wireless communication systems such as LPC, PCM, ADPCM, CELP and VSELP.,2013-03-08 16:58:15,下载15次
New-Folder-(4).rar - In this paper the analysis of the compression process was performed by comparing the compressed signal against the original signal. To do this the most powerful speech analysis and compression techniques such as Linear Predictive Coding (LPC) and Discrete Wavelet Transform (DWT) was implemented using MATLAB. Here nine samples of spoken words are collected from different speakers and are used for implementation. The results obtained from LPC were compared with other compression technique called Discrete Wavelet Transform. Finally the results were evaluated in terms of compressed ratio (CR), Peak signal-to-noise ratio (PSNR) and Normalized root-mean square error (NRMSE).The result shows that DWT performance was better for these samples than the LPC method.,2013-03-08 16:55:26,下载21次
New-Folder-(4).rar - In this research two automatic video annotation techniques are considered. The first technique uses ontology to reduce the semantic gap during video retrieval and other performs a group based image retrieval using video files. The proposed algorithm uses GIR algorithm to create similar image group. From this refined set of images, SIFT features are extracted and the steps used by ASVA algorithm is performed to annotate the video in a semantic fashion. The Automatic Semantic based Video Annotation algorithm performs annotation in three steps. The first step calculates the video similarity using SIFT features, sentence and synonym analysis is performed in the second to find similar meaning annotations and finally the conjunction of the sentences are analyzed to increase the certainty of each annotation using Concept Net.,2013-03-08 16:46:41,下载15次