Evaluating Performance of Compressive sensing for speech signal with Combined Basis
نویسنده
چکیده
In Compressed Sensing (CS) framework, reconstruction of a signal relies on the knowledge of the sparse basis & measurement matrix used for sensing. Most of the studies so far focus on the application of CS in fields of images, radar, astronomy and Speech. This paper introduce new approach called combined basis that is made by separating voiced and unvoiced parts and applying different basis for both parts from given speech and shows detailed comparison of them with LPC basis and orthogonal gaussian matrix applied on 8 KHz sampled speech signal. Also it shows improved results of Combined DCT and LPC basis compared to LPC and Combined DFT and LPC basis. Performance of these basis has been compared with Mean square error, Signal to noise ratio and Perceptual Evaluation of Speech Quality (PESQ) parameters. Keywords— Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), Linear Prediction Coding (LPC), Orthogonal Gaussian Matrix
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