IEEE Signal Processing - March 2018 - 15

Microphone
Inputs

STM32F446RE

BMD-300

Voltage
Regulator

coLuMbIa unIversIty

BLE
Antenna

Figure 4. The prototype intelligent pedestrian safety headset and connected smartphone.

The signal processing pipeline starts
in the analog domain with four channels of microelectromechanical system
(MEMS) microphones. Signals from the
analog sensors are amplified, sampled,
filtered, and used to compute a number of
useful features, such as cross-correction,
zero-crossing, and signal power. On the
digital side, frequency domain features
relating to sound are computed using fast
Fourier transforms, cosine transforms,
and other techniques. "In particular, we
created a set of frequency domain features specific to cars," Jiang explains.
ML classifiers are used to produce
various outputs, such as the directions
and distances of approaching vehicles.
These outputs are then converted into
signals that generate the sounds-
beeps-directed at the headset's wearer.
The signal processing pipeline is divided into two parts: the headset and the

smartphone. "We decided to locally process the multichannel audio data inside
the headset and generate audio features
that are much smaller in size, but still able
to carry sufficient information for the ML
algorithms, inside the smartphone to classify locations of vehicles," Jiang says.
"We decided to build a custom integrated
circuit to perform cross-correlation to
further reduce the power consumption on
the embedded side.
Design decisions, one of the project's
biggest technical challenges, center around
a tradeoff between accuracy, latency, and
power. "In terms of which sensors to use,
we decided to go with MEMS microphones
instead of other sensors, such as a camera
or light detection and ranging, because of
the extremely limited energy budget on a
smartphone/headset," Jiang says.
The biggest challenge the team currently faces is improving the system's
IEEE Signal Processing Magazine

|

March 2018

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accuracy in various environments. "Since
part of our system relies on training
models, sometimes it works in a particular type of environment, but does
not work as well in another," Jiang says.
"We need to work on making our system work well in various environments,
from quiet rural streets to cities with tall
buildings to the sides of highways."
The researchers are also looking to
make the system more compact and less
power hungry. "Ultimately, we want this
to look no different from any existing
headphones on the market, yet provide
the additional benefit of danger alerts,"
Jiang says.

Author
John Edwards ( jedwards@johnedwards
media.com) is a technology writer based
in the Phoenix, Arizona, area.
SP
15



Table of Contents for the Digital Edition of IEEE Signal Processing - March 2018

Contents
IEEE Signal Processing - March 2018 - Cover1
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IEEE Signal Processing - March 2018 - Contents
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