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@ARTICLE{Schoepe:1022166,
author = {Schoepe, Thorben and Gutierrez-Galan, Daniel and
Dominguez-Morales, Juan P and Greatorex, Hugh and
Jimenez-Fernandez, Angel and Linares-Barranco, Alejandro and
Chicca, Elisabetta},
title = {{C}losed-loop sound source localization in neuromorphic
systems},
journal = {Neuromorphic computing and engineering},
volume = {3},
number = {2},
issn = {2634-4386},
address = {Bristol},
publisher = {IOP Publishing Ltd.},
reportid = {FZJ-2024-01287},
pages = {024009 -},
year = {2023},
abstract = {Sound source localization (SSL) is used in various
applications such as industrial noise-control,speech
detection in mobile phones, speech enhancement in hearing
aids and many more. Newestvideo conferencing setups use SSL.
The position of a speaker is detected from the difference in
theaudio waves received by a microphone array. After
detection the camera focuses onto the locationof the
speaker. The human brain is also able to detect the location
of a speaker from auditorysignals. It uses, among other
cues, the difference in amplitude and arrival time of the
sound wave atthe two ears, called interaural level and time
difference. However, the substrate and
computationalprimitives of our brain are different from
classical digital computing. Due to its low powerconsumption
of around 20 W and its performance in real time the human
brain has become agreat source of inspiration for emerging
technologies. One of these technologies is
neuromorphichardware which implements the fundamental
principles of brain computing identified until todayusing
complementary metal-oxide-semiconductor technologies and new
devices. In this work wepropose the first neuromorphic
closed-loop robotic system that uses the interaural time
differencefor SSL in real time. Our system can successfully
locate sound sources such as human speech. In aclosed-loop
experiment, the robotic platform turned immediately into the
direction of the soundsource with a turning velocity
linearly proportional to the angle difference between sound
sourceand binaural microphones. After this initial turn, the
robotic platform remains at the direction ofthe sound
source. Even though the system only uses very few resources
of the available hardware,consumes around 1 W, and was only
tuned by hand, meaning it does not contain any learning
atall, it already reaches performances comparable to other
neuromorphic approaches. The SSLsystem presented in this
article brings us one step closer towards neuromorphic
event-basedsystems for robotics and embodied computing.},
cin = {PGI-15},
ddc = {621.3},
cid = {I:(DE-Juel1)PGI-15-20210701},
pnm = {5234 - Emerging NC Architectures (POF4-523)},
pid = {G:(DE-HGF)POF4-5234},
typ = {PUB:(DE-HGF)16},
UT = {WOS:001064496000001},
doi = {10.1088/2634-4386/acdaba},
url = {https://juser.fz-juelich.de/record/1022166},
}