The Experts below are selected from a list of 5562 Experts worldwide ranked by ideXlab platform
Tianshu Qu - One of the best experts on this subject based on the ideXlab platform.
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Audio lossless coding/decoding method using basis Pursuit Algorithm
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Wenxin He, Tianshu QuAbstract:Basis Pursuit Algorithm is one of the most popular methods of sparse coding. The goal of the Algorithm is to represent signal using as few coefficients as possible, which is suitable for acoustic signal compression. This paper presents a lossless coding/decoding method using the basis Pursuit Algorithm. In this method, wavelet packets bases were used to compose the dictionary because of their natural sparse property. Experimental results are obtained by comparing the proposed method with the four popular lossless coding/decoding methods using various types of acoustic signals. The results show that the proposed method is competitive with the well-known methods for lossless compression, in terms of compression ratio and computational efficiency.
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ICASSP - Audio lossless coding/decoding method using basis Pursuit Algorithm
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Wenxin He, Tianshu QuAbstract:Basis Pursuit Algorithm is one of the most popular methods of sparse coding. The goal of the Algorithm is to represent signal using as few coefficients as possible, which is suitable for acoustic signal compression. This paper presents a lossless coding/decoding method using the basis Pursuit Algorithm. In this method, wavelet packets bases were used to compose the dictionary because of their natural sparse property. Experimental results are obtained by comparing the proposed method with the four popular lossless coding/decoding methods using various types of acoustic signals. The results show that the proposed method is competitive with the well-known methods for lossless compression, in terms of compression ratio and computational efficiency.
Wenxin He - One of the best experts on this subject based on the ideXlab platform.
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Audio lossless coding/decoding method using basis Pursuit Algorithm
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Wenxin He, Tianshu QuAbstract:Basis Pursuit Algorithm is one of the most popular methods of sparse coding. The goal of the Algorithm is to represent signal using as few coefficients as possible, which is suitable for acoustic signal compression. This paper presents a lossless coding/decoding method using the basis Pursuit Algorithm. In this method, wavelet packets bases were used to compose the dictionary because of their natural sparse property. Experimental results are obtained by comparing the proposed method with the four popular lossless coding/decoding methods using various types of acoustic signals. The results show that the proposed method is competitive with the well-known methods for lossless compression, in terms of compression ratio and computational efficiency.
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ICASSP - Audio lossless coding/decoding method using basis Pursuit Algorithm
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Wenxin He, Tianshu QuAbstract:Basis Pursuit Algorithm is one of the most popular methods of sparse coding. The goal of the Algorithm is to represent signal using as few coefficients as possible, which is suitable for acoustic signal compression. This paper presents a lossless coding/decoding method using the basis Pursuit Algorithm. In this method, wavelet packets bases were used to compose the dictionary because of their natural sparse property. Experimental results are obtained by comparing the proposed method with the four popular lossless coding/decoding methods using various types of acoustic signals. The results show that the proposed method is competitive with the well-known methods for lossless compression, in terms of compression ratio and computational efficiency.
Lenan Wu - One of the best experts on this subject based on the ideXlab platform.
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Size of the dictionary in matching Pursuit Algorithm
IEEE Transactions on Signal Processing, 2004Co-Authors: Qiangsheng Liu, Qiao Wang, Lenan WuAbstract:The matching Pursuit Algorithm has been successfully applied in many\nareas such as data compression and pattern recognition. The performance\nof matching Pursuit is closely related to the selection of the dictionary.\nIn this paper, we propose an Algorithm to estimate the optimal dictionary\ndistribution ratio and discuss the decay of the norm of residual\nsignal in matching Pursuit when the coefficients are quantized by\na uniform scalar quantizer. It is proposed that if the approximation\nerror E and the dimension of the space N are given, the optimal size\nof the dictionary and the optimal quantizer step should be obtained\nby minimizing the number of bits required to store the matching Pursuit\nrepresentation of any signal in the space to satisfy the error bound.
Christine Guillemot - One of the best experts on this subject based on the ideXlab platform.
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Sparse approximation with an orthogonal complementary matching Pursuit Algorithm
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Gagan Rath, Christine GuillemotAbstract:This paper presents the orthogonal extension of the recently introduced complementary matching Pursuit (CMP) Algorithm for sparse approximation. The CMP Algorithm is analogous to the matching Pursuit (MP) but done in the row-space of the dictionary matrix. It suffers from a similar sub-optimality as the MP. The orthogonal complementary matching Pursuit Algorithm (OCMP) presented here tries to remove this sub-optimality by updating the coefficients of all selected atoms at each iteration. Its development from the CMP follows the same procedure as of the orthogonal matching Pursuit (OMP). In contrast with OMP, the residual errors resulting from the OCMP may not be orthogonal to all the atoms selected up to the respective iteration. Though the residual energy may increase over the OMP during the first iterations, it is shown that, compared with OMP, the convergence speed is increased in the subsequent iterations and the sparsity of the solution vector is improved.
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ICASSP - Sparse approximation with an orthogonal complementary matching Pursuit Algorithm
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Gagan Rath, Christine GuillemotAbstract:This paper presents the orthogonal extension of the recently introduced complementary matching Pursuit (CMP) Algorithm for sparse approximation [1]. The CMP Algorithm is analogous to the matching Pursuit (MP) but done in the row-space of the dictionary matrix. It suffers from a similar sub-optimality as the MP. The orthogonal complementary matching Pursuit Algorithm (OCMP) presented here tries to remove this sub-optimality by updating the coefficients of all selected atoms at each iteration. Its development from the CMP follows the same procedure as of the orthogonal matching Pursuit (OMP). In contrast with OMP, the residual errors resulting from the OCMP may not be orthogonal to all the atoms selected up to the respective iteration. Though the residual energy may increase over the OMP during the first iterations, it is shown that, compared with OMP, the convergence speed is increased in the subsequent iterations and the sparsity of the solution vector is improved.
Wenbo Li - One of the best experts on this subject based on the ideXlab platform.
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A modified greedy analysis Pursuit Algorithm for the cosparse analysis model
Numerical Algorithms, 2016Co-Authors: Jicheng Li, Wenbo LiAbstract:In the past decade, the sparse representation synthesis model has been deeply researched and widely applied in signal processing. Recently, a cosparse analysis model has been introduced as an interesting alternative to the sparse representation synthesis model. The sparse synthesis model pay attention to non-zero elements in a representation vector x, while the cosparse analysis model focuses on zero elements in the analysis representation vector Ωx. This paper mainly considers the problem of the cosparse analysis model. Based on the greedy analysis Pursuit Algorithm, by constructing an adaptive weighted matrix Wk?1, we propose a modified greedy analysis Pursuit Algorithm for the sparse recovery problem when the signal obeys the cosparse model. Using a weighted matrix, we fill the gap between greedy Algorithm and relaxation techniques. The standard analysis shows that our Algorithm is convergent. We estimate the error bound for solving the cosparse analysis model, and then the presented simulations demonstrate the advantage of the proposed method for the cosparse inverse problem.
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ICNC - The reweighed greedy analysis Pursuit Algorithm for the cosparse analysis model
2015 11th International Conference on Natural Computation (ICNC), 2015Co-Authors: Jicheng Li, Wenbo LiAbstract:Recently, a cosparse analysis model has been introduced as an interesting alternative to the sparse representation synthesis model. This model is focused on zero elements in the analysis representation vector rather than non-zero elements. Hence, finding cosparse solutions is a problem of important significance in signal processing. In this paper, we construct an adaptive weighted matrix in the greedy analysis Pursuit Algorithm and propose the reweighed greedy analysis Pursuit (ReGAP) Algorithm for cosparse signal reconstruction with noise. Using a weighted matrix, we fill the gap between greedy and convex relaxation techniques. Theoretical analysis shows that our Algorithm is convergent. We estimate the error bound of ReGAP Algorithm with cosparse analysis model, and then simulation results demonstrate that our Algorithm is feasible and effective.
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The reweighed greedy analysis Pursuit Algorithm for the cosparse analysis model
2015 11th International Conference on Natural Computation (ICNC), 2015Co-Authors: Jicheng Li, Wenbo LiAbstract:Recently, a cosparse analysis model has been introduced as an interesting alternative to the sparse representation synthesis model. This model is focused on zero elements in the analysis representation vector rather than non-zero elements. Hence, finding cosparse solutions is a problem of important significance in signal processing. In this paper, we construct an adaptive weighted matrix in the greedy analysis Pursuit Algorithm and propose the reweighed greedy analysis Pursuit (ReGAP) Algorithm for cosparse signal reconstruction with noise. Using a weighted matrix, we fill the gap between greedy and convex relaxation techniques. Theoretical analysis shows that our Algorithm is convergent. We estimate the error bound of ReGAP Algorithm with cosparse analysis model, and then simulation results demonstrate that our Algorithm is feasible and effective.
