Comparison of Cubic and Fibonacci Activation Functions in Speech Signal Seperation using Independent Component Analysis
AbstractThe cubic activation function has been used extensively in audio signal separation algorithms. The intention of this paper is to measure the quality of separation for this activation function and compare its performance to that ofthe Fibonacci. The setup entails the use of the Natural Gradient Algorithm (NGA) to separate two mixed signals into their original components using the Independent Component Analysis (ICA). The NGA used is formulated using instantaneous Blind Signal Processing (BSP). The design uses a 2 x 2 Multiple Input Multiple Output (MIMO) system to accept the two speech signals, mix them and separate them to retain their original form or their filtered version. Two activation functions; the Cubic and Fibonacci are used interchangeably. The results are compared by analyzing the magnitude-squared coherence of the input and separated signals. The results show that Fibonacci is best suited to separate speech signals when applied in NGA than the cubic activation function.
Jun 13, 2016
How to Cite
CHIBOLE, James; HEYWOOD, A.; NDUNGU, Edward. Comparison of Cubic and Fibonacci Activation Functions in Speech Signal Seperation using Independent Component Analysis. Proceedings of Sustainable Research and Innovation Conference, [S.l.], p. 231-237, june 2016. ISSN 2079-6226. Available at: <http://sri.jkuat.ac.ke/ojs/index.php/proceedings/article/view/433>. Date accessed: 22 feb. 2018.
Blind Signal Processing (BSS); Independent Component Analysis (ICA); Natural Gradient Algorithm (NGA); Activation Functions; magnitude-Squared Coherence.
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