DENOISE SPEECH USING DEEP LEARNING TECHNIQUES
Objective
Develop a CNN-based system to denoise speech signals, improving audio clarity and communication, aiding voice assistants, telecommunication, and assistive devices, achieving 92% accuracy in removing noise.
Abstract
To enhance the clarity and comprehensibility of audio recordings impacted by interference or noise. Deep Learning Networks for speech denoising is an exciting project with broad applications. By utilizing the power of cutting-edge neural networks, we can improve voice assistants and telecommunication as well as improve audio signal quality and communication clarity for those who are hard of hearing. This endeavor is a step towards a more inclusive and connected world, not only an innovation. It is utilized in the real world to improve voice-controlled systems, audio recordings and broadcasting, hearing aids and assistive devices, and communication. In this implementation, we have used Convolutional Neural Networks (CNN) for denoising the speech signals. CNN is a deep learning model that needs training on the samples of speech. So firstly, we have trained the CNN model using the original speech samples that contains raw speech data. Later, we have tested its accuracy by providing a noise contaminated speech signal. The CNN model have produced the denoised output speech sample. We got an accuracy of over 92% for the CNN model.
Keywords: Deep learning Networks, Speech Denoising, Convolutional Neural Networks (CNN), Training, Testing.
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
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
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.
