Publication: Blind bounded source separation using neural networks with local learning rules
Program
KU-Authors
KU Authors
Co-Authors
Pehlevan, Cengiz
Advisor
Publication Date
2020
Language
English
Type
Conference proceeding
Journal Title
Journal ISSN
Volume Title
Abstract
An important problem encountered by both natural and engineered signal processing systems is blind source separation. In many instances of the problem, the sources are bounded by their nature and known to be so, even though the particular bound may not be known. To separate such bounded sources from their mixtures, we propose a new optimization problem, Bounded Similarity Matching (BSM). A principled derivation of an adaptive BSM algorithm leads to a recurrent neural network with a clipping nonlinearity. The network adapts by local learning rules, satisfying an important constraint for both biological plausibility and implementability in neuromorphic hardware. © 2020 IEEE.
Description
Source:
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:
The Institute of Electrical and Electronics Engineers, Signal Processing Society
Keywords:
Subject
Acoustics, Engineering, Electrical electronic engineering