Team Members
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Mahmoud Mamdouh
Team Leader
![](https://cscgp.miuegypt.edu.eg/wp-content/uploads/2024/06/cropped-Maged-Magdy-2018-03102-Mahmoud-Mamdouh.jpg)
Maged Magdy
Team Member
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Omar Adel
Team Member
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Ahmed Mohamed
Team Member
Supervisors
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Dr. Diaa Salama
Professor
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Eng. Nada Nofal
Teaching Assistant
Abstract
In the realm of audio enhancement, traditional methods of signal processing are now competing with the innovative wave of deep learning and Generative AI technologies and solutions, which are shortening the gap in audio enhancement in an unprecedented way. This is particularly evident when dealing with vintage recordings from older audio equipment and in challenging noisy environments. Our proposed system embraces a multimodal approach that harnesses the power of generative AI, integrating audio Super Resolution and Retrieval Based Voice Conversion (RVC), to elevate the overall and perceived quality of user-uploaded audio files. Recognizing that superior audio quality is not the product of a single technology, our system integrates a suite of advanced techniques to deliver a truly enhanced auditory experience. It is designed to be accessible across various platforms, ensuring compatibility with a wide range of devices and browsers, and providing an intuitive interface for users with no advanced audio concepts to effortlessly upload and improve their audio content and perform authentic voice conversion.
System Objectives
Develop a high-fidelity vocal replication system using Retrieval-based voice conversion (RVC) and text-to-speech sound synthesis (TTS), to ensure a natural and superior quality that closely resembles the original recordings.
Employ Audio super-resolution technology (Audio SR) to enhance the bit-depth and sample rate of audio files and recordings, contributing to a significantly improved audio experience.
Enhance the overall and perceived quality of user-uploaded audio files by integrating and devoloping a multimodal approach using generative AI and deep learning technologies, encompassing Super Resolution, and Retrieval Based Voice Conversion (RVC).
Introduce innovative configurations and parameter adjustments to optimize transfer learning between technologies, achieving greater accuracy in audio replication and fidelity, recognizing that superior audio quality requires a combination of techniques rather than relying on a single technology alone.
Develop an intuitive web application designed for simplicity and ease of use, enabling users with no advanced audio concepts to enhance audio quality and perform voice conversions effortlessly.
System Scope
The system shall develop a multi-modal audio enhancement platform that integrates text-to-speech sound synthesis (TTS), retrieval-based voice conversion (RVC), and audio super-resolution (Audio SR).
The system shall employ deep learning, machine learning algorithms, and generative AI techniques for high-fidelity vocal replication and the improvement of perceived audio quality.
The system shall provide an intuitive interface, allowing users to easily upload and enhance audio files, ensuring a seamless experience for those without advanced audio knowledge.
The system shall enable users to share their enhanced audio and videos with others, providing a platform for feedback and ratings on exported files.
The system shall offer additional features such as speech-to-text functionality and enhancements to the sound quality of uploaded videos.
The system shall be accessible across various platforms, ensuring compatibility with a wide range of web browsers.
Documents and Presentations
Proposal
You will find here the documents and presentation for our proposal.
SRS
You will find here the documents and presentation for our SRS.
SDD
You will find here the documents and presentation for our SDD.
Thesis
You will find here the documents and presentation for our Thesis
Document
Presentation
Accomplishments
Publications
Competitions
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Competition Title
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