Work

Blind Bounded Source Separation Using Neural Networks with Local Learning Rules

Year: 2020

Type: conference-paper

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.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp40776.2020.9053114

Authors: Alper T. Erdogan, Cengiz Pehlevan

Institutions: Koç University, Harvard University

Language: English

Topic: Blind Source Separation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3015244520

FWCI: 0.313

Cites: 15

Cited by: 3

Related to: 10