Independent component analysis algorithm FPGA design to perform real-time blind source separation

Uwe Meyer-Baese; Crispin Odom; Guillermo Botella; Anke Meyer-Baese

doi:10.1117/12.2178521

20 May 2015 Independent component analysis algorithm FPGA design to perform real-time blind source separation

Uwe Meyer-Baese, Crispin Odom, Guillermo Botella, Anke Meyer-Baese

Proceedings Volume 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII; 94960X (2015) https://doi.org/10.1117/12.2178521
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

The conditions that arise in the Cocktail Party Problem prevail across many fields creating a need for of Blind Source Separation. The need for BSS has become prevalent in several fields of work. These fields include array processing, communications, medical signal processing, and speech processing, wireless communication, audio, acoustics and biomedical engineering. The concept of the cocktail party problem and BSS led to the development of Independent Component Analysis (ICA) algorithms. ICA proves useful for applications needing real time signal processing. The goal of this research was to perform an extensive study on ability and efficiency of Independent Component Analysis algorithms to perform blind source separation on mixed signals in software and implementation in hardware with a Field Programmable Gate Array (FPGA). The Algebraic ICA (A-ICA), Fast ICA, and Equivariant Adaptive Separation via Independence (EASI) ICA were examined and compared. The best algorithm required the least complexity and fewest resources while effectively separating mixed sources. The best algorithm was the EASI algorithm. The EASI ICA was implemented on hardware with Field Programmable Gate Arrays (FPGA) to perform and analyze its performance in real time.

Citation Download Citation

Uwe Meyer-Baese, Crispin Odom, Guillermo Botella, and Anke Meyer-Baese "Independent component analysis algorithm FPGA design to perform real-time blind source separation", Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960X (20 May 2015); https://doi.org/10.1117/12.2178521

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