We present an overview of the biomedical part of the 2010 community-based Signal Separation Evaluation Campaign (SiSEC2010), coordinated by the authors. In addition to the audio tasks which have been evaluated in the previous SiSEC, SiSEC2010 considered several biomedical tasks. Here, three biomedical datasets from molecular biology (gene expression profiles) and neuroscience (EEG) were contrib...

Urine is the waste fraction from households which contains the largest amounts of nutrients. In Sweden it contains approximately 70% of the nitrogen and 50% of the phosphorous and potassium in all household waste and wastewater fractions. During the 1990-ies, urine separation has been thoroughly investigated in waterborne systems in Sweden. Measurements have shown that between 50% and 85% of th...

We propose a new framework, called piecewise linear separation, for blind source separation of possibly degenerate mixtures, including the extreme case of a single mixture of several sources. Its basic principle is to : 1/ decompose the observations into “components” using some sparse decomposition/nonlinear approximation technique; 2/ perform separation on each component using a “local” separa...

Blind source separation (BSS) can be seen as a generalization of denoising a noisy signal when several sensors are available. Each of them records the same physical phenomenon in a different way: such a diversity is then useful to separate the present signals for instance by independent component analysis (ICA) or sparse component analysis (SCA) [1]. The main objective of speech separation/extr...

A maximum likelihood (ML) approach is used to separate the instantaneous mixtures of temporally correlated, independent sources with neither preliminary transformation nor a priori assumption about the probability distribution of the sources. A Markov model is used to represent the joint probability density of successive samples of each source. The joint probability density functions are estima...

STATISTICS Ulf Lindgren, Henrik Sahlin and Holger Broman Department of Applied Electronics Chalmers University of Technology S-412 96 G oteborg, Sweden E-mail:[email protected], [email protected], [email protected] ABSTRACT It is often assumed that blind separation of dynamically mixed sources can not be accomplished with second order statistics. In this paper it is shown that sep...

Source separation, or computational auditory scene analysis, attempts to extract individual acoustic objects from input which contains a mixture of sounds from different sources, altered by the acoustic environment. Unmixing algorithms such as ICA and its extensions recover sources by reweighting multiple observation sequences, and thus cannot operate when only a single observation signal is av...

Recent work in blind source separation applied to anechoic mixtures of speech allows for reconstruction of sources that rarely overlap in a time-frequency representation. While the assumption that speech mixtures do not overlap significantly in time-frequency is reasonable, music mixtures rarely meet this constraint, requiring new approaches. We introduce a method that uses spatial cues from an...