KEYWORDS: Digital signal processing, Analog electronics, Nerve, Neurons, Signal processing, Field programmable gate arrays, Acoustics, Mirrors, Computing systems, Electronics
We are developing low-power microcircuitry that implements classification and direction finding systems of very small
size and small acoustic aperture. Our approach was inspired by the fact that small mammals are able to localize sounds
despite their ears may be separated by as little as a centimeter. Gerbils, in particular are good low-frequency localizers,
which is a particularly difficult task, since a wavelength at 500 Hz is on the order of two feet. Given such signals, crosscorrelation-
based methods to determine direction fail badly in the presence of a small amount of noise, e.g. wind noise
and noise clutter common to almost any realistic environment. Circuits are being developed using both analog and
digital techniques, each of which process signals in fundamentally the same way the peripheral auditory system of
mammals processes sound. A filter bank represents filtering done by the cochlea. The auditory nerve is implemented
using a combination of an envelope detector, an automatic gain stage, and a unique one-bit A/D, which creates what
amounts to a neural impulse. These impulses are used to extract pitch characteristics, which we use to classify sounds
such as vehicles, small and large weaponry from AK-47s to 155mm cannon, including mortar launches and impacts. In
addition to the pitchograms, we also use neural nets for classification.
This paper describes the flow of scientific and technological achievements beginning with a stationary "small, smart,
biomimetic acoustic processor" designed for DARPA that led to a program aimed at acoustic characterization and
direction finding for multiple, mobile platforms. ARL support and collaboration has allowed us to adapt the core
technology to multiple platforms including a Packbot robotic platform, a soldier worn platform, as well as a vehicle
platform. Each of these has varying size and power requirements, but miniaturization is an important component of the
program for creating practical systems which we address further in companion papers. We have configured the system to
detect and localize gunfire and tested system performance with live fire from numerous weapons such as the AK47, the
Dragunov, and the AR15. The ARL-sponsored work has led to connections with Natick Labs and the Future Force
Warrior program, and in addition, the work has many and obvious applications to homeland defense, police, and civilian needs.
KEYWORDS: Biomimetics, Signal processing, Digital signal processing, Nerve, Source localization, Acoustics, Computing systems, Systems modeling, Data processing, Neurons
In this paper a real-time sound source localizing system is proposed, which is based on previously developed
mammalian auditory models. Traditionally, following the models, which use interaural time delay (ITD) estimates,
the amount of parallel computations needed by a system to achieve real-time sound source localization is a limiting
factor and a design challenge for hardware implementations. Therefore a new approach using a time-shared architecture
implementation is introduced.
The proposed architecture is a purely sample-base-driven digital system, and it follows closely the continuous-time
approach described in the models. Rather than having dedicated hardware on a per frequency channel basis, a specialized
core channel, shared for all frequency bands is used. Having an optimized execution time, which is much less than the
system's sample rate, the proposed time-shared solution allows the same number of virtual channels to be processed as
the dedicated channels in the traditional approach. Hence, the time-shared approach achieves a highly economical and
flexible implementation using minimal silicon area. These aspects are particularly important in efficient hardware
implementation of a real time biomimetic sound source localization system.
KEYWORDS: Digital signal processing, Analog electronics, Neurons, Transistors, Biomimetics, Field programmable gate arrays, Nerve, Algorithm development, Signal processing, Signal to noise ratio
Biomimetic signal processing that is functionally similar to that performed by the mammalian peripheral auditory system
consists of several stages. The concatenated stages of the system each favor differing types of hardware
implementations. Ideally, the front-end would be an implementation of the mammalian cochlea, which is a tapered
nonlinear, traveling-wave amplifier. It is not a good candidate for standard digital implementations. The AM
demodulator can be implemented using digital or analog designs. The Automatic Gain Control (AGC) stage is highly
unusual. It requires filtering and multiplication in a closed-loop configuration, with bias added at each of two
concatenated stages. Its implementation is problematic in DSP, FPGA, full custom digital VLSI, and analog VLSI. The
one-bit A/D (also called the "spiking neuron"), while simple at face value, involves a complicated triggering mechanism,
which is amenable to DSP, FPGA, and custom digital but computationally intense, and is suited to an analog VLSI
implementation.
Currently, we have several hardware embodiments of the biomimetic system. The RedOwl application occupies about
160 cubic inches in volume at the present time. A DSP approach can compute 15 channels for two ears for three A/D
categories using Analog Devices Tiger SHARC-201 DSP chips within a system size estimated to be on the order of 30
cubic inches. BioMimetic Systems, Inc., a Boston University startup company is developing an FPGA solution. Within
the university, we are also pursuing both a custom digital ASIC route and a current-mode analog ASIC.
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