LOUD A 1020-node modular microphone array and beamformer for intelligent computing spaces.pdf
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LOUD A 1020-node modular microphone array and beamformer for intelligent computing spaces
LOUD: A 1020-Node Modular Microphone
Array and Beamformer for Intelligent
Computing Spaces
Eugene Weinstein, Kenneth Steele, Anant Agarwal, and James Glass
MIT Computer Science and Artificial Intelligence Laboratory
32 Vassar Street
Cambridge, MA 02139 USA
{ecoder,steele,agarwal,glass}@
Abstract. Ubiquitous computing environments are characterized by an
unbounded amount of noise and crosstalk. In these environments, tradi-
tional methods of sound capture are insufficient, and array microphones
are needed in order to obtain a clean recording of desired speech. In this
work, we have designed, implemented, and tested LOUD, a novel 1020-
node microphone array utilizing the Raw tile parallel processor architec-
ture [1] for computation. To the best of our knowledge, this is currently
the largest microphone array in the world. We have explored the uses
of the array within ubiquitous computing scenarios by implementing an
acoustic beamforming algorithm for sound source amplification in a noisy
environment, and have obtained preliminary results demonstrating the
efficacy of the array. From one to 1020 microphones, we have shown a
13.7dB increase in peak SNR on a representative utterance, an 87.2%
drop in word error rate with interferer present, and an 89.6% drop in
WER without an interferer.
1 Introduction
The interaction between humans and computers has been a central focus of ubiq-
uitous computing research in recent times. In particular, communication through
speech has been extensively explored as a method for making human-computer
interaction more natural. However, computer recognition of human speech per-
forms when a recording can be made without the presence of much ambient noise
or crosstalk. Seeking to create a natural setting, ubiquitous computing environ-
ments tend to fall in this category of situations where natural-interaction speech
recognition is a challenging problem. When significant levels of noise are present,
or several humans are talking at the same t
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