FEATURE EXTRACTION AND ANALYSIS OF SPEECH SIGNAL BASED ON EMPIRICAL MODE DECOMPOSITION
Objective
This study focuses on improving speech signal processing by using Empirical Mode Decomposition (EMD) to extract reliable features from dynamic, noisy speech signals for better speech recognition accuracy.
Abstract
The use of voice, an analogue signal with specific information, to gather and transmit information in social interactions has become more prevalent. Transforming complicated speech settings into useful speech information is the aim of speech signal processing. Over time, the speech signal's characteristics change significantly. An important first step in speech signal processing is to guarantee that the qualities of the voice signal are mostly unchanged in a short amount of time. The accuracy and robustness of feature extraction from speech signals also have an impact on the rate of speech recognition. Speech signal feature extraction is therefore essential in applications involving speech signal processing. Another way to understand the distinct benefits of the Empirical Mode Decomposition (EMD) over Intrinsic Mode Functions (IMFs) is to contrast its time-frequency resolution with that of the fractional Fourier transform on a noisy background.
Keywords – Empirical Mode Decomposition (EMD), Speech Signal, Feature Extraction, Speech Recognition, Speech Signal Processing.
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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