6 accepted papers
We propose a novel convex penalty for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adjusted for the signal of interest by minimizing the mixed ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmln…
In this paper, we propose an over-determined sound source separation method considering the sparsity of impulse responses. Conventional methods, including independent low-rank matrix analysis (ILRMA), have mainly focused on design of realistic sound generation models, but the separation performance…
Speech and audio signal processing frequently requires to recover a time-domain signal from the magnitude of a spectrogram. Conventional methods inversely transform the magnitude spectrogram with a phase spectrogram recovered by the Griffin-Lim algorithm or its accelerated versions. The short-time F…
The nonlinear inverse problem of 2-D phase unwrapping consists in estimating an image, while its pixel values are observed modulo 2π. A variational formulation is considered, which consists in minimizing an energy, convex or not, under the nonconvex data fidelity constraints. We propose a new convex…
Splines are piecewise polynomials and widely used for interpolation and smoothing of observed data, due to their flexibility and optimality in the sense of certain variational problems for one-dimensional (1D) data. However, spline interpolation and smoothing are applicable only to the estimation of…
The compressed sensing using dictionary learning has led to state-of-the-art results for magnetic resonance imaging (MRI) reconstruction from highly under-sampled measurements. Dictionary learning had been considered time-consuming especially when the patch size or the number of training patches is…