FPGA Implementation of ICA for mixed signal using Frog Leap Optimization Algorithm

Speech verification is a bio-metric system which performs the computing task of validating a user identity using the characteristics feature extracted from their speech samples. The independent component analysis (ICA) is one of the major trending technologies to differentiate signals from their mixed quantity. ICA is one of the technique to extract a signal from a compound signal. The fundamental approach of ICA is to find a suitable representation of multivariate data. The representation is often sought as a linear transformation of the original data. Linear transformation methods include principal component analysis (PCA), factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent. This kind of analysis captures the essential structure of the data in many applications including feature extraction and signal separation. The use of evolutionary computation based optimization such as Frog Leap Optimization Algorithms are stochastic search methods that mimics the identity of natural biological evolution with additional operations of crossover and feedback resolves the permutation ambiguity to a large extent.