1,688 research outputs found
Audio Inpainting
(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published version: IEEE Transactions on Audio, Speech and Language Processing 20(3): 922-932, Mar 2012. DOI: 10.1090/TASL.2011.2168211
Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure
In this paper, we study the missing sample recovery problem using methods
based on sparse approximation. In this regard, we investigate the algorithms
used for solving the inverse problem associated with the restoration of missed
samples of image signal. This problem is also known as inpainting in the
context of image processing and for this purpose, we suggest an iterative
sparse recovery algorithm based on constrained -norm minimization with a
new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index,
is a simplified version of the Structural SIMilarity (SSIM) index, which is
convex and error-sensitive. The optimization problem incorporating this
criterion, is then solved via Alternating Direction Method of Multipliers
(ADMM). Simulation results show the efficiency of the proposed method for
missing sample recovery of 1D patch vectors and inpainting of 2D image signals
CS reconstruction of the speech and musical signals
The application of Compressive sensing approach to the speech and musical
signals is considered in this paper. Compressive sensing (CS) is a new approach
to the signal sampling that allows signal reconstruction from a small set of
randomly acquired samples. This method is developed for the signals that
exhibit the sparsity in a certain domain. Here we have observed two sparsity
domains: discrete Fourier and discrete cosine transform domain. Furthermore,
two different types of audio signals are analyzed in terms of sparsity and CS
performance - musical and speech signals. Comparative analysis of the CS
reconstruction using different number of signal samples is performed in the two
domains of sparsity. It is shown that the CS can be successfully applied to
both, musical and speech signals, but the speech signals are more demanding in
terms of the number of observations. Also, our results show that discrete
cosine transform domain allows better reconstruction using lower number of
observations, compared to the Fourier transform domain, for both types of
signals
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