  The rapidly growing adoption of sensor-enabled smartphones has greatly fueled
the proliferation of applications that use phone sensors to monitor user
behavior. A central sensor among these is the microphone which enables, for
instance, the detection of valence in speech, or the identification of
speakers. Deploying multiple of these applications on a mobile device to
continuously monitor the audio environment allows for the acquisition of a
diverse range of sound-related contextual inferences. However, the cumulative
processing burden critically impacts the phone battery.
  To address this problem, we propose DSP.Ear - an integrated sensing system
that takes advantage of the latest low-power DSP co-processor technology in
commodity mobile devices to enable the continuous and simultaneous operation of
multiple established algorithms that perform complex audio inferences. The
system extracts emotions from voice, estimates the number of people in a room,
identifies the speakers, and detects commonly found ambient sounds, while
critically incurring little overhead to the device battery. This is achieved
through a series of pipeline optimizations that allow the computation to remain
largely on the DSP. Through detailed evaluation of our prototype implementation
we show that, by exploiting a smartphone's co-processor, DSP.Ear achieves a 3
to 7 times increase in the battery lifetime compared to a solution that uses
only the phone's main processor. In addition, DSP.Ear is 2 to 3 times more
power efficient than a naive DSP solution without optimizations. We further
analyze a large-scale dataset from 1320 Android users to show that in about
80-90% of the daily usage instances DSP.Ear is able to sustain a full day of
operation (even in the presence of other smartphone workloads) with a single
battery charge.
