Noise Cancellation in Speech Signal Using Nonlinear Adaptive Filters
Objective
Develop and compare nonlinear adaptive filters—Signed Regressor LMS, Signed LMS, and Signed Error LMS—to remove noise from speech signals. Analyze and identify the most effective technique for optimal denoising.
Abstract
The need to eliminate noise from speech signals is quite great. In order to remove noise from the voice signals, we constructed nonlinear adaptive filters such as the Signed Regressor LMS, Signed LMS, and Signed Error LMS algorithms and compared them in this research. The final denoised signal will be found using the coefficients of the three techniques. Compared to the other two algorithms, the Signed Regressor LMS algorithm will work better.
Keywords: Speech Signals, Nonlinear Adaptive Filters, Signed Regressor LMS, Signed LMS and Signed Error LMS algorithms.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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