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The paper introduces Sona, a mobile system for real-time, selective sound attenuation targeting multiple overlapping sound sources. Sona employs a target-conditioned neural pipeline that allows users to specify and attenuate bothersome sounds while preserving desired audio, addressing the limitations of existing single-target noise cancellation systems. Benchmarking and in-situ studies demonstrate Sona's low-latency performance and its ability to reduce bothersome sounds while maintaining environmental awareness.
Imagine a world where your phone intelligently filters out the sounds that trigger your noise sensitivity, letting you focus on what matters.
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.