Delay-Coordinates Embeddings as a Data Mining Tool for Denoising Speech Signals

This paper utilizes techniques from the theory of non-linear dynamical systems to define a notion of embedding threshold estimators. More specifically it uses delay-coordinates embeddings of sets of coefficients of the measured signal (in some chosen frame) as a data mining tool to separate structures that are likely to be generated by signals belonging to some predetermined data set. It describes a particular variation of the embedding threshold estimator implemented in a windowed Fourier frame, and it applies it to speech signals heavily corrupted with the addition of several types of white noise. Its experimental work seems to suggest that, after training on the data sets of interest; these estimators perform well for a variety of white noise processes and noise intensity levels.