In a number of signal processing applications, problem formulations based on the 1 norm as a sparsity inducing signal prior lead to simple algorithms with good performance. Howeve...
We develop a framework to detect when certain sounds are present in a mixed audio signal. We focus on the regime where out of a large number of possible sounds, a small but unknow...
We introduce a class of inverse problem estimators computed by mixing adaptively a family of linear estimators corresponding to different priors. Sparse mixing weights are calcula...
Abstract. We apply sparse, fast and flexible adaptive lapped orthogonal transforms to underdetermined audio source separation using the time-frequency masking framework. This norm...
Robust clustering of data into overlapping linear subspaces is a common problem. Here we consider one-dimensional subspaces that cross the origin. This problem arises in blind sour...