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Telecommunication Networks Group
Design, Development and Assembling of an inclusive and accessible audio interface
keywords ACCESSIBILITY, AUDIO PROCESSING, ELECTRONICS DEVICES, SOUND PRODUCTION, WEB APPLICATIONS
Reference persons CRISTINA EMMA MARGHERITA ROTTONDI
External reference persons Pietro Buccellato (PhD student)
Research Groups Telecommunication Networks Group
Description Context
The tools available in a recording studio that enable music production are numerous, and the way artists/producers interact with them is crucial. Currently, the music equipment market is predominantly geared toward users with full visual, auditory, and motor capabilities, leaving a significant gap in the availability of equipment that allows people with disabilities to operate independently, regardless of the required level of professionalism.
This thesis aims to design, develop and assembly an inclusive and accessible audio interface, enabling a wide range of users with different types of disabilities (mild or severe motor, visual, and/or speech impairments) to interact with it through a web app that supports voice recognition and eye tracking, offering full autonomy in music production for the user.
Objectives and Proposed Approach
1) Studying the state of the art and state key problem areas to be addressed
2) Designing and assembling of the hardware front-end for the audio input acquisition
3) Development and integration of firmware
4) Development of the web app
5) Integration of Machine learning techniques or algorithms to ease the settings of the audio board based on the quality of the sound, acoustics, type of instruments and/or many more attributes.
6) Testing and validation.
Along all the steps there will be an active collaboration with members of the research group who have already developed part of the system for different purposes.
Learning outcomes and skill development
• Research methodologies
• Inclusive design
• Knowledge of electronic components
• Prototyping and assembling
• Electronic signal analysis
• Low-level programming
• Firmware debugging
• Data analysis
Required skills • Fundamentals of hardware design (Cadence or Altium)
• Fundamentals of firmware development (C Language)
• Application of machine learning techniques (Python).
Deadline 11/11/2025
PROPONI LA TUA CANDIDATURA