Real-Time Speech Signal Denoising Using Wavelet Transform Techniques
Objective
The objective of this study is to enhance voice signal denoising by improving the Wavelet Threshold method in combination with a Moving Average Filter (MAF), effectively reducing noise in non-stationary random speech signals through multi-resolution analysis and wavelet reconstruction.
Abstract
In the period of the slow development of artificial intelligence, humans provide voice commands to machines to perform tasks. For a computer to recognize human voice signals, there must be less noise interference, which is why voice signal denoising technology is so important. Since most speech signals are non-stationary random signals, standard denoising algorithms have difficulty differentiating them from other sounds. The objective of this study is to improve the Wavelet Threshold in conjunction with a Moving Average Filter (MAF) that was used for denoising in this type of non-stationary random signal processing. Wavelet reconstruction is then used to recover the original voice signal before MAF is used to remove noise. It does this by utilizing the benefits of multi-resolution analysis.
Keywords: Speech Signals, Denoising Algorithm, Simulation and Wavelet Threshold, Moving Average Filter (MAF), Signal-To-Noise Ratio.
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
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