Sona实时筛选噪音，只降烦人声保留有用音，适合敏感人群。
Sona: Real-Time Multi-Target Sound Attenuation for Noise Sensitivity
- 基于目标感知神经网络，可同时处理多个声音源
- 实测延迟低，10人现场测试显著降低困扰声
- 无需重训练，用户自定义新声音类别
对噪音敏感者而言，日常声景常令人不堪忍受。现有工具如主动降噪虽能缓解不适，但会屏蔽周围所有声音，影响环境感知。本文提出Sona，一种运行于移动端的交互式实时声景调控系统，可选择性衰减令人不适的声音，同时保留期望音频。Sona采用目标条件神经流水线，支持多声源同时衰减，突破了以往系统仅限单目标的限制。系统在设备端实时运行，可通过现场音频示例扩展声音类别，无需重新训练。研究基于68名敏感者的形成性调研，并通过技术基准测试与10名参与者的实地实验验证：Sona实现低延迟、多目标衰减，适用于实时聆听，显著减少烦人声响的同时维持对环境的感知。结果表明，这类个人AI系统有望通过调控现实声景，提升舒适度与社会参与感。
原文摘要 · Abstract (English)
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.
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