  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.
