458 research outputs found
Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation
To date a number of studies have shown that receptive field shapes of early
sensory neurons can be reproduced by optimizing coding efficiency of natural
stimulus ensembles. A still unresolved question is whether the efficient coding
hypothesis explains formation of neurons which explicitly represent
environmental features of different functional importance. This paper proposes
that the spatial selectivity of higher auditory neurons emerges as a direct
consequence of learning efficient codes for natural binaural sounds. Firstly,
it is demonstrated that a linear efficient coding transform - Independent
Component Analysis (ICA) trained on spectrograms of naturalistic simulated
binaural sounds extracts spatial information present in the signal. A simple
hierarchical ICA extension allowing for decoding of sound position is proposed.
Furthermore, it is shown that units revealing spatial selectivity can be
learned from a binaural recording of a natural auditory scene. In both cases a
relatively small subpopulation of learned spectrogram features suffices to
perform accurate sound localization. Representation of the auditory space is
therefore learned in a purely unsupervised way by maximizing the coding
efficiency and without any task-specific constraints. This results imply that
efficient coding is a useful strategy for learning structures which allow for
making behaviorally vital inferences about the environment.Comment: 22 pages, 9 figure
A Tutorial on Independent Component Analysis
Independent component analysis (ICA) has become a standard data analysis
technique applied to an array of problems in signal processing and machine
learning. This tutorial provides an introduction to ICA based on linear algebra
formulating an intuition for ICA from first principles. The goal of this
tutorial is to provide a solid foundation on this advanced topic so that one
might learn the motivation behind ICA, learn why and when to apply this
technique and in the process gain an introduction to this exciting field of
active research
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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