ENHANCING SPEECH FOR HEARING IMPAIRED AIDS THROUGH CNN
Objective
To develop a real-time CNN-based speech enhancement system for hearing aid users, using log power spectrum features to improve speech intelligibility and quality while ensuring low latency on smartphones.
Abstract
This paper presents a Speech Enhancement (SE) technique employing a convolutional neural network (CNN) trained using log power spectrum features to improve speech quality for Hearing Aid (HA) users. Designed as a real-time smartphone application, the proposed system effectively reduces background noise, enhancing speech intelligibility in noisy environments. By utilizing the log power spectrum as the sole input representation, the approach simplifies the feature extraction process while maintaining high performance. The model is optimized for low latency and minimal processing delay, enabling efficient deployment on mobile devices. Comparative evaluations with existing SE methods demonstrate significant improvements in speech quality and intelligibility. The primary contribution lies in the practical deployment of a lightweight, real-time SE solution tailored for HA users, offering a balance between accurate noise suppression and computational efficiency. Experimental results confirm the effectiveness of the proposed method in challenging acoustic settings, highlighting its potential to enhance everyday auditory experiences for individuals with hearing impairments.
Keywords: Speech Enhancement, Hearing Aid, Convolutional Neural Network, Noise Reduction, Real-Time Processing, Speech Quality, Speech Intelligibility, Mobile Application, Audio Features.
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 R2022b.
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.
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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