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Sparse Time-frequency Representation via Atomic Norm Minimization by Kusano, Tsubasa; Yatabe, Kohei; Oikawa, Yasuhiro is a scholarly article available to read on EtoBox.

What is Sparse Time-frequency Representation via Atomic Norm Minimization about?

Nonstationary signals are commonly analyzed and processed in the time-frequency (T-F) domain that is obtained by the discrete Gabor transform (DGT). The T-F representation obtained by DGT is spread due to windowing, which may degrade the performance of T-F domain analysis and processing. To obtain a well-localized T-F representation, sparsity-aware methods using $\ell_1$-norm have been studied. However, they need to discretize a continuous parameter onto a grid, which causes a model mismatch. In this paper, we propose a method of estimating a sparse T-F representation using atomic norm. The atomic norm enables sparse optimization without discretization of continuous parameters. Numerical experiments show that the T-F representation obtained by the proposed method is sparser than the conventional methods.

Author

Kusano, Tsubasa; Yatabe, Kohei; Oikawa, Yasuhiro

Published

2021

Language

EN