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.

翻译：对于噪声敏感人群而言，日常环境中的声音可能令人难以承受。现有工具如主动降噪技术虽能通过抑制整个声学环境来减轻不适感，但往往以牺牲对周围人和事件的感知为代价。我们提出Sona——一个用于实时声景调节的交互式移动系统，它能在保留期望音频的同时选择性衰减令人困扰的声音。Sona基于目标条件化神经管道构建，支持同时衰减多个重叠声源，突破了先前系统仅能处理单一目标的局限。该系统可在设备端实时运行，并通过现场音频样本支持用户可扩展的声音类别而无需重新训练。Sona的设计源于一项针对68名噪声敏感者的形成性研究。通过技术基准测试及一项包含10名参与者的现场研究，我们证明Sona能够实现适用于实时聆听的低延迟多目标衰减，在显著降低恼人声音的同时保持对周围环境的感知。这些结果指向一类新型个人AI系统——通过调节真实世界的声学环境来支持用户舒适度与社交参与。