Non-negative data arise in a variety of important signal processing domains, such as power spectra of signals, pixels in images, and count data. We introduce a novel non-negative dynamical system model for sequences of such data. The model we propose is called non-negative dynamical system (NDS), and bridges two active fields, dynamical systems and nonnegative matrix factorization (NMF). Its formulation follows that of linear dynamical systems, but the observation and the latent variables are assumed non-negative, the linear transforms are assumed to involve non-negative coefficients, and the additive random innovations both for the observation and the latent variables are replaced by multiplicative random innovations. The software includes code for training and testing, as well as a simple framework for applying this model to the task of speech enhancement.
To download the software, please enter some information about yourself, then review and agree to MERL's research-only licensing terms.