Semi Blind Source Separation for Application in Machine Learning

Abstract

Unsupervised learning is a class of problems in machine learning which seeks to determine how the data are organized. Unsupervised learning encompasses many other techniques that seek to summarize and explain key features of the data. One form of unsupervised learning is blind source separation (BSS). BSS is a class of computational data analysis techniques for revealing hidden factors that underlie sets of measurements or signals. BSS assumes a statistical model whereby the observed multivariate data, typically given as a large database of samples, are assumed to be linear or nonlinear mixtures of some unknown latent variables. The mixing coefficients are also unknown. Sometimes more prior information about the sources is available or is induced into the model, such as the form of their probability densities, their spectral contents, etc. Then the term blind is often replaced by semiblind. This chapter reports the semi BSS machine learning applications on audio and bio signal processing.