Sona: Real-Time Multi-Target Sound Attenuation for Noise Sensitivity

Abstract

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.

Figures (5)

• Figure 1: Distribution of participants' reported bothersomeness across sound categories (Top 5, sorted by ratings of very bothersome or above).
• Figure 2: Sona’s three-layer system architecture. The Context Reasoning Layer classifies ambient sounds and supports two pathways: it suggests known sounds for immediate suppression, and flags unknown sounds that match the user’s sensitivity profiles for later personalization. The Live Listen Layer attenuates user-selected targets on-device while preserving the surrounding auditory scene. The Personalization Layer manages sensitivity profiles, custom sound classes, and recordings of novel trigger sounds, which can later be used to create custom sound classes for attenuation.
• Figure 3: Sona's trigger discovery pipeline. When an unsupported sound is detected, the system generates a description and matches it against user-defined sensitivity profiles to determine whether it should be saved for later personalization.
• Figure 4: Sona's User Interface. (A) In Live Listen, Sona surfaces suggestions for detected sounds, lets users select target sounds, and supports real-time adjustment of attenuation strength. (B) When an unfamiliar sound matches a stored sensitivity profile, Sona prompts the user to save it for later personalization. (C) Users can manage custom sound classes and sensitivity profiles. (D) Users can create a reusable custom class by recording audio samples.
• Figure 5: The rating distribution for Sona (1 = strongly disagree, 7 = strongly agree).