Popular music is often composed of an accompaniment and a lead component, the latter
typically consisting of vocals. Filtering such mixtures to extract one or both components has …

Monaural source separation is important for many real world applications. It is challenging
because, with only a single channel of information available, without any constraints, an …

With pervasive sensors continuously collecting and storing massive amounts of information,
there is no doubt this is an era of data deluge. Learning from these large volumes of data is …

Deep clustering is the first method to handle general audio separation scenarios with
multiple sources of the same type and an arbitrary number of sources, performing …

We introduce a robust mixing model to describe hyperspectral data resulting from the
mixture of several pure spectral signatures. The new model extends the commonly used …

Extracting latent low-dimensional structure from high-dimensional data is of paramount
importance in timely inference tasks encountered with “Big Data” analytics. However …

Parsimony, including sparsity and low rank, has been shown to successfully model data in
numerous machine learning and signal processing tasks. Traditionally, such modeling …

Monaural source separation is important for many real world applications. It is challenging
since only single channel information is available. In this paper, we explore using deep …

Given the noiseless superposition of a low-rank matrix plus the product of a known fat
compression matrix times a sparse matrix, the goal of this paper is to establish deterministic …

We propose a unified and systematic framework for performing online nonnegative matrix
factorization in the presence of outliers. Our framework is particularly suited to large-scale …