Authors :
Pavan Kumar; Shreya K Shetty; Prajwal Diwakar; Swasthik Narayan Bhat; Dony D’souza
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3nRH1t2
DOI :
https://doi.org/10.5281/zenodo.6814658
Abstract :
Without knowing all of the mixing matrix and
source signal properties, blind source separation is a
process of differentiating sources of signals mixed with
time, location, and frequency. This algorithm is widely
chosen since it not only has a fast convergence time but
also performs well in separation. Blind Source Separation
(BSS) is a method of isolating sources from a combination
without first determining which sources are present. To
accomplish the above objective, several methods have
been offered, one of which is ICA (independent
component analysis). Duet presupposes that signals are
non-overlapping in the temporal domain, while
independent component analysis asserts that signals are
linearly separable. In the event of blind source
separation, ICA is used. They are quite similar to the
older traditional fast fixed- point (FastICA) methods
based on kurtosis, but they differ in that they are more
efficient in terms of processing performance than the
corresponding latter ones, which is especially noticeable
when the number of samples is large. The Cocktail Party
Problem degrades the quality of discourse in this
situation. Cocktail party issues are defined as a mix of
several sources of speech signals picked up by a
microphone. ICA, which has the ability to split many
voice signals into individual ones, can be used to solve the
aforesaid problem. Gradient and FastICA algorithms are
used to apply the principle of negentropy by maximizing
non- Gaussianity approaches in ICA. The MATLAB
results show that Fast ICA has a faster execution time
than gradient with the small number of iterations.
Independent component analysis was used to estimate
source signals using mutual information minimization.
Maximizing non-Gaussianity improves performance in
certain cases. The use of kurtosis and negentropy are two
strategies for maximizing non-Gaussianity. Because
kurtosis is the most vulnerable to outliers and is a
computationally resilient process, negentropy is more
dependable. For syntactic verification of mechanical and
structural systems, the two routine BSS solutions are ICA
and SOBI. The traditional FastICA methods are still the
most widely used tools for estimating independent
components. FastICA functions can be used in a variety
of computer languages, including MATLAB.
Keywords :
Blind Source Seperation, FastICA Algorithm, PCA Algorithm, Second Order Blind Identification, Negentropy, Kurtosis.
Without knowing all of the mixing matrix and
source signal properties, blind source separation is a
process of differentiating sources of signals mixed with
time, location, and frequency. This algorithm is widely
chosen since it not only has a fast convergence time but
also performs well in separation. Blind Source Separation
(BSS) is a method of isolating sources from a combination
without first determining which sources are present. To
accomplish the above objective, several methods have
been offered, one of which is ICA (independent
component analysis). Duet presupposes that signals are
non-overlapping in the temporal domain, while
independent component analysis asserts that signals are
linearly separable. In the event of blind source
separation, ICA is used. They are quite similar to the
older traditional fast fixed- point (FastICA) methods
based on kurtosis, but they differ in that they are more
efficient in terms of processing performance than the
corresponding latter ones, which is especially noticeable
when the number of samples is large. The Cocktail Party
Problem degrades the quality of discourse in this
situation. Cocktail party issues are defined as a mix of
several sources of speech signals picked up by a
microphone. ICA, which has the ability to split many
voice signals into individual ones, can be used to solve the
aforesaid problem. Gradient and FastICA algorithms are
used to apply the principle of negentropy by maximizing
non- Gaussianity approaches in ICA. The MATLAB
results show that Fast ICA has a faster execution time
than gradient with the small number of iterations.
Independent component analysis was used to estimate
source signals using mutual information minimization.
Maximizing non-Gaussianity improves performance in
certain cases. The use of kurtosis and negentropy are two
strategies for maximizing non-Gaussianity. Because
kurtosis is the most vulnerable to outliers and is a
computationally resilient process, negentropy is more
dependable. For syntactic verification of mechanical and
structural systems, the two routine BSS solutions are ICA
and SOBI. The traditional FastICA methods are still the
most widely used tools for estimating independent
components. FastICA functions can be used in a variety
of computer languages, including MATLAB.
Keywords :
Blind Source Seperation, FastICA Algorithm, PCA Algorithm, Second Order Blind Identification, Negentropy, Kurtosis.
