Evaluation of Sparsity-based Methods for Parameterized Source Separation

Date

2020-10-07

Author

Baskaya, Hasan Can
Öktem, Sevinç Figen

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Parametric reconstruction problems arise in many areas such as array processing, wireless communication, source separation, and spectroscopy. In a parametric recovery problem, the unknown model parameters in each superimposed signal are estimated from noisy observations. Sparsity-based methods used in compressive sensing are also applied to parametric recovery problems. These methods discretize the parameter space to form a dictionary whose atoms correspond to candidate parameter values, represent the data as a linear combination of small number of dictionary atoms, and then solve the resulting linear inverse problem. In this work, we first consider different the sparsity-based methods for parametric recovery problems with multiple measurement vectors. We then analyze and evaluate these methods for the parameterized source separation problem.

Subject Keywords

Parametric reconstruction, Inverse problems, Sparse recovery, Source separation

URI

https://hdl.handle.net/11511/73629
https://ieeexplore.ieee.org/document/9302138/keywords#keywords

DOI

https://doi.org/10.1109/SIU49456.2020.9302138

Conference Name

28th Signal Processing and Communications Applications Conference

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

H. C. Baskaya and S. F. Öktem, “Evaluation of Sparsity-based Methods for Parameterized Source Separation,” presented at the 28th Signal Processing and Communications Applications Conference, Türkiye, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73629.