dc.contributor.author |
G, Pushpalatha |
|
dc.contributor.author |
Sivakumar, B. |
|
dc.date.accessioned |
2024-02-10T17:58:45Z |
|
dc.date.available |
2024-02-10T17:58:45Z |
|
dc.date.issued |
2024-02-08 |
|
dc.identifier.issn |
2210-142X |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5420 |
|
dc.description.abstract |
This paper presents a novel computational framework for blind audio source separation (BASS) that enhances existing
Independent Component Analysis (ICA) with an adaptive swarm intelligence algorithm (ASIA) in over-determined scenario to find an
optimal de-mixing matrix that could efficiently separate mixed signals. The proposed ASIA methodology addresses the challenges of
optimal parameter determination in stochastic optimization process of swarm intelligence approach for an estimation of the precise
unmixing matrix. In order to ensure the separated signals are as independent as possible in BASS task, a complex and non-convex
optimization problem is formulated where the unmixing matrix is customized to minimize mutual information and maximize the
non-Gaussianity of the signals. To solve our optimization problem the study introduces a weighted combination of negentropy
and cross-correlation in the fitness function of the proposed ASIA. Additionally, it incorporates an adaptive inertia weight and
velocity clamping mechanism into the traditional swarm optimization technique to addresses the challenges associated with parameter
determination in stochastic optimization techniques. This unique approach of proposed framework ensures maximum statistical
independence of the separated signals from the unknown mixed signals. Overall analysis of experimental outcome demonstrate that
the proposed framework exhibits superior blind separation of mixed audio signals, showcasing enhanced computational efficiency and
de-mixing accuracy compared to conventional baseline approaches. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Audio Signal; Mixed Signal; Blind Source Separation, ICA; Swarm optimization |
en_US |
dc.title |
A Novel Blind Audio Source Separation Utilizing Adaptive Swarm Intelligence and Combined Negentropy-Cross Correlation Optimization |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/160192 |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1247 |
en_US |
dc.pageend |
1258 |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authoraffiliation |
Research Scholar, Department of Telecommunication Engineering, Dr. Ambedkar Institute of Technology |
en_US |
dc.contributor.authoraffiliation |
Professor, Department of Telecommunication Engineering, Dr. Ambedkar Institute of Technology |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |