Design of optimal matrices for compressive sensing: Application to environmental sounds. Bouchhima, B., Amara, R., & Alouane, M. T. In *2015 23rd European Signal Processing Conference (EUSIPCO)*, pages 130-134, Aug, 2015.

Paper doi abstract bibtex

Paper doi abstract bibtex

In a compressive sensing context, we propose a solution for a full learning of the dictionary composed of the sparsity basis and the measurement matrix. The sparsity basis learning process is achieved using Empirical Mode Decomposition (EMD) and Hilbert transformation. EMD being a data-driven decomposition method, the resulting sparsity basis shows high sparsifying capacities. On the other hand, a gradient method is applied for the design of the measurement matrix. The method integrates the dictionary normalization into the target function. It is shown to support large scale problems and to have a good convergence and high performance. The evaluation of the whole approach is done on a set of environmental sounds, and is based on a couple of key criteria: sparsity degree and incoherence. Experimental results demonstrate that our approach achieves well with regards to mutual coherence reduction and signal reconstruction at low sparsity degrees.

@InProceedings{7362359, author = {B. Bouchhima and R. Amara and M. T. Alouane}, booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)}, title = {Design of optimal matrices for compressive sensing: Application to environmental sounds}, year = {2015}, pages = {130-134}, abstract = {In a compressive sensing context, we propose a solution for a full learning of the dictionary composed of the sparsity basis and the measurement matrix. The sparsity basis learning process is achieved using Empirical Mode Decomposition (EMD) and Hilbert transformation. EMD being a data-driven decomposition method, the resulting sparsity basis shows high sparsifying capacities. On the other hand, a gradient method is applied for the design of the measurement matrix. The method integrates the dictionary normalization into the target function. It is shown to support large scale problems and to have a good convergence and high performance. The evaluation of the whole approach is done on a set of environmental sounds, and is based on a couple of key criteria: sparsity degree and incoherence. Experimental results demonstrate that our approach achieves well with regards to mutual coherence reduction and signal reconstruction at low sparsity degrees.}, keywords = {compressed sensing;Hilbert transforms;matrix algebra;signal reconstruction;dictionary learning;signal reconstruction;dictionary normalization;data-driven decomposition;Hilbert transformation;EMD;empirical mode decomposition;sparsity basis learning process;measurement matrix;environmental sounds;compressive sensing;optimal matrices design;Signal processing algorithms;Coherence;Dictionaries;Convergence;Compressed sensing;Gradient methods;Europe;Compressive Sensing;EMD;Environmental Sounds;Sparsity;Measurement Matrix;Incoherence}, doi = {10.1109/EUSIPCO.2015.7362359}, issn = {2076-1465}, month = {Aug}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104363.pdf}, }

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