Adaptive Sound Profiles: Using Machine Learning

Authors: Kiran Srivastava, Shreyansh Singh, Syed Shahzeb Ali, Uddhav Garg

Abstract: The personal audio devices often fall short because they can’t keep up with the constantly changing condition, we use them in. People end up adjusting the volume over and over-whether the background noise suddenly gets louder, the listening environment shifts, or their own habits change. All of this interrupts the experience and makes listening less enjoyable. This study explores how to fix that by introducing a fully autonomous, personalised audio system called adaptive sound profiles. At the core of this approach is a blend of machine-learning-based personalisation, real-time sound monitoring, and a hands-free gesture control system. It uses two microphones: one listens to what’s coming out of the speaker, and the other listens to what’s happening around the user. By comparing these two audio signals, the system can automatically adjust the volume so that the sound stays clear and comfortable, no matter what’s going on in the environment. The framework doesn’t stop at sound alone. It also considers factors like time of day and predicted user behaviour. A built-in Memory Component keeps track of each person’s listening preferences and applies them on its own, allowing the system to grow and adapt with the user over time. Instead of just reacting when the noise around you suddenly changes the system learns from what is happening in the environment and prepares in advance. The result is a smart, privacy-friendly and personalized way to control audio making listening feel smoother and much more enjoyable.

DOI: http://doi.org/10.5281/zenodo.21701470