The objective of audio inpainting is to fill a gap in an signal. This ideally done by reconstructing the original signal or, at least, inferring meaningful surrogate We propose novel approach applying sparse modeling time-frequency (TF) domain. In particular, we devise dictionary learning technique which learns from reliable parts around with goal obtain representation increased TF sparsity. ba...

We investigate the use of sparse coding and dictionary learning in the context of multitask and transfer learning. The central assumption of our learning method is that the tasks parameters are well approximated by sparse linear combinations of the atoms of a dictionary on a high or infinite dimensional space. This assumption, together with the large quantity of available data in the multitask ...

Locality-Constrained Discriminative Learning and Coding Report Title This paper explores the enhancement by locality constraint to both learning and coding schemes, more specifically, discriminative low-rank dictionary learning and auto-encoder. Previous Fisher discriminative based dictionary learning has led to interesting results by learning more discerning sub-dictionaries. Also, the low-ran...

In this paper a new dictionary learning algorithm for multidimensional data is proposed. Unlike most conventional dictionary learning methods which are derived for dealing with vectors or matrices, our algorithm, named KTSVD, learns a multidimensional dictionary directly via a novel algebraic approach for tensor factorization as proposed in [3, 12, 13]. Using this approach one can define a tens...