Blind source separation is the computation underlying the cocktail party effect––a partygoer
can distinguish a particular talker's voice from the ambient noise. Early studies indicated that …

The human auditory system has the ability to segregate complex auditory scenes into a
foreground component and a background, allowing us to listen to specific speech sounds …

In this paper, fully connected recurrent neural networks are investigated for blind separation
of sources. For these networks, a new class of unsupervised on-line learning algorithms are …

An important problem in neuroscience is to understand how brains extract relevant signals
from mixtures of unknown sources, ie, perform blind source separation. To model how the …

Extraction of latent sources of complex stimuli is critical for making sense of the world. While
the brain solves this blind source separation (BSS) problem continuously, its algorithms …

Humans have an exceptional ability to extract specific audio streams of interest in a noisy
environment; this is known as the cocktail party effect. It is widely accepted that this ability is …

The abundance of stimuli typical of a state fair, however, does not lend itself to studying the
mind's fusion of the five senses: a process called sensory integration. Researchers tend to …

Blind source separation—the extraction of independent sources from a mixture—is an
important problem for both artificial and natural signal processing. Here, we address a …

A striking feature of many sensory processing problems is that there appear to be many
more neurons engaged in the internal representations of the signal than in its transduction …

Independent component analysis (or blind source separation) is assumed to be an essential
component of sensory processing in the brain and could provide a less redundant …