Sona: Real-Time Multi-Target Sound Attenuation for Noise Sensitivity
This addresses the problem of overwhelming soundscapes for people with noise sensitivity, offering a more selective alternative to existing noise cancellation tools.
The paper tackles the problem of noise sensitivity by developing Sona, a real-time mobile system that selectively attenuates bothersome sounds while preserving desired audio, achieving low-latency, multi-target attenuation suitable for live listening and enabling meaningful reductions in bothersome sounds as shown in an in-situ study with 10 participants.
For people with noise sensitivity, everyday soundscapes can be overwhelming. Existing tools such as active noise cancellation reduce discomfort by suppressing the entire acoustic environment, often at the cost of awareness of surrounding people and events. We present Sona, an interactive mobile system for real-time soundscape mediation that selectively attenuates bothersome sounds while preserving desired audio. Sona is built on a target-conditioned neural pipeline that supports simultaneous attenuation of multiple overlapping sound sources, overcoming the single-target limitation of prior systems. It runs in real time on-device and supports user-extensible sound classes through in-situ audio examples, without retraining. Sona is informed by a formative study with 68 noise-sensitive individuals. Through technical benchmarking and an in-situ study with 10 participants, we show that Sona achieves low-latency, multi-target attenuation suitable for live listening, and enables meaningful reductions in bothersome sounds while maintaining awareness of surroundings. These results point toward a new class of personal AI systems that support comfort and social participation by mediating real-world acoustic environments.