AI & DSP Noise Cancellation for Intercoms
Pubblicato il 13-08-2026 - STMicroelectronics Italia in Agrate Brianza
OUR STORY
At STMicroelectronics, we believe in the power of technology to drive innovation and make a positive impact on people, businesses, and society. As a global semiconductor company, our advanced technologies and chips form the hidden foundation of the world we live in today.
When you join ST, you will be part of a global business with more than 115 nationalities, present in 40 countries, and comprising over 50,000 diverse and dedicated creators and makers of technology around the world.
Developing technologies takes more than talent: it takes amazing people who understand collaboration and respect. People with passion and the desire to disrupt the status quo, drive innovation, and unlock their own potential.
Embark on a journey with us, where you can innovate for a future that we want to make smarter and greener, in a responsible and sustainable way. Our technology starts with you.
About The Project
Intercom systems are used in environments where communication must remain clear even in the presence of strong and dynamic background noise.
In many practical use cases,
the noise is non-stationary and can vary rapidly over time, making its suppression particularly challenging.
Recent approaches combining digital signal processing (DSP) and machine learning have shown strong potential for improving noise cancellation performance. However, there is still room to explore more effective methods, especially for dynamic and real-world intercom scenarios.
In this internship, the student will work within an R&D; team on the research and development of noise cancellation solutions for intercom applications. The objective is to investigate and compare DSP-based and AI-based approaches, with the goal of improving speech clarity and robustness in challenging acoustic conditions.
YOUR ROLE
- You contribute to the research and development of a noise cancellation solution for intercom applications.
- You take part in exploring and evaluating different DSP and machine learning st