Acoustic sensing manifests great potential in various applications that encompass health monitoring, gesture interface and imaging by leveraging the speakers and microphones on smart devices. However, in ongoing research and development in acoustic sensing, one problem is often overlooked: the same speaker, when used concurrently for sensing and other traditional applications (like playing music), could cause interference in both making it impractical to use in the real world. The strong ultrasonic sensing signals mixed with music would overload the speaker's mixer. To confront this issue of overloaded signals, current solutions are clipping or down-scaling, both of which affect the music playback quality and also sensing range and accuracy. To address this challenge, we propose CoPlay, a deep learning based optimization algorithm to cognitively adapt the sensing signal. It can 1) maximize the sensing signal magnitude within the available bandwidth left by the concurrent music to optimize sensing range and accuracy and 2) minimize any consequential frequency distortion that can affect music playback. In this work, we design a deep learning model and test it on common types of sensing signals (sine wave or Frequency Modulated Continuous Wave FMCW) as inputs with various agnostic concurrent music and speech. First, we evaluated the model performance to show the quality of the generated signals. Then we conducted field studies of downstream acoustic sensing tasks in the real world. A study with 12 users proved that respiration monitoring and gesture recognition using our adapted signal achieve similar accuracy as no-concurrent-music scenarios, while clipping or down-scaling manifests worse accuracy. A qualitative study also manifests that the music play quality is not degraded, unlike traditional clipping or down-scaling methods.

翻译：声学感知通过利用智能设备的扬声器和麦克风在健康监测、手势界面和成像等应用中展现出巨大潜力。然而，在当前的声学感知研究与开发中，一个常被忽视的问题是：同一扬声器若同时用于感知和其他传统应用（如播放音乐），可能产生相互干扰，使其在现实世界中难以实用。强超声感知信号与音乐混合会过载扬声器的混音器。针对信号过载问题，现有解决方案是限幅或降幅缩放，但这两种方法均会影响音乐播放质量以及感知范围和精度。为应对这一挑战，我们提出CoPlay，一种基于深度学习的优化算法，用于认知性地调整感知信号。该算法能够：1）在并发音乐留下的可用带宽内最大化感知信号幅度，以优化感知范围和精度；2）最小化可能影响音乐播放的任何衍生频率失真。本研究设计了一个深度学习模型，并以常见类型的感知信号（正弦波或调频连续波FMCW）作为输入，在多种未知并发音乐和语音条件下进行测试。首先，我们评估模型性能以展示生成信号的质量。随后，我们在现实世界中开展下游声学感知任务的实地研究。一项包含12名用户的研究证明，使用自适应信号进行呼吸监测和手势识别的准确率与无并发音乐场景相当，而限幅或降幅缩放方法则表现出更低的准确率。定性研究还表明，与传统的限幅或降幅缩放方法不同，音乐播放质量未受影响。