Speech Signal Denoising Algorithm and Simulation Based on Wavelet Threshold
Objective
The project aims to address the critical issue of enhancing the quality of speech signals corrupted by various types of noise, such as background noise, interference, or distortions, to improve the overall intelligibility and usability of the audio data.
Abstract
In the era of the gradual development of artificial intelligence, people use sound to control the machine to complete the work. When the machine recognizes the human voice signal, the voice must be a signal with less noise interference, so the voice signal denoising technology is particularly important. Common speech signals are non-stationary random signals, mixed with various noises, and it is difficult to filter out common denoising methods. Aiming at this kind of non-stationary random signal denoising processing, this paper uses the characteristics of wavelet transform multi-resolution analysis to improve the wavelet threshold denoising algorithm and uses the improved wavelet threshold function to remove noise and get the original speech signal. The simulation results show that the improved wavelet threshold denoising algorithm has better performance. When analyzing non-stationary random signals, the denoising effect under different signal-to-noise ratios is very significant.
Keywords: Speech Signals, Denoising Algorithm, Simulation and Wavelet Threshold.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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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
