Functional source separation from magnetoencephalographic signals

Blind Source Separation: Recovering Signals from Mixtures

Score-Informed Source Separation for Music Signals

In recent years, the processing of audio recordings by exploiting additional musical knowledge has turned out to be a promising research direction. In particular, additional note information as specified by a musical score or a MIDI file has been employed to support various audio processing tasks such as source separation, audio parameterization, performance analysis, or instrument equalization...

Blind Source Separation: Application to biomedical signals

Blind Source Separation (BSS) is a prominent problem in signal processing. In the past few decades, it was applied to many fields, in which separation of compound signals, simultaneously observed by different sensors, is of interest. The problem can be considered as built-up of three physical elements: sources (also called transmitters), sensors (also called receivers) and communication channel...

Blind Source Separation of Compressively Sensed Signals

We present an approach to simultaneously separate and reconstruct signals from a compressively sensed linear mixture. We assume that the signals have a common sparse representation. The approach combines classical Compressive Sensing (CS) theory with a linear mixing model. Since Blind Source Separation (BSS) from a linear mixture is only possible up to permutation and scaling, factoring out the...

Bayesian source separation of fMRI signals

In analyzing the results of functional magnetic resonance imaging, the identification of significant activation in voxels is a crucial task. In computing the activation level, a standard method is to select an assumed to be known reference function and perform a multiple regression of the time courses on it and a linear trend. Once the linear trend is found, the correlation between the assumed ...