Annual: 2019

AS026 »
iOwlT: Sound Geolocalization System
📁Machine Learning
👤Matheus Farias
 (Universidade Federal de Pernambuco (UFPE))
📅Jun 30, 2019
Regional Final
Community Award

217


👀 1299   💬 10

AS026 » iOwlT: Sound Geolocalization System

Description

iOwlT is a geolocalization sound system based on the nature of prey searches by an nocturne owl. Using the technique of multilateration of signals, widely used in telecommunications, and the phase shift of a signal detected by distinct sensors well distributed in space, it is possible, with the knowledge of algebra and physics, to prove that in an N-dimensional space, just N+1 detectors are needed to accurately determine the origin of the event. As the real life has 3 dimensions only 4 sound sensors determines the location of an event.

Combining the power of the parallel processing, achieved with the use of the FPGA from DE-10 Nano board to deal with the appropriate simultaneous treatment of the audio signals by, mainly, adaptive digital filters (they adapt to the sound signals obtained in order to optimize their processing), and the use of machine learning algorithms trained to recognize the desired event, the present project aims to design an embedded system that could be coupled to a vehicle, which detects the location of gun-shooting events, that when identified will be displayed in a mobile application.

It can be used in urban areas to detect sources of gun shots and even in forbidden hunting areas to identify possible hunters, the problem to be solved has applications that do not restrict the location of the source and of a specific sound, it can be adapted to the recognition of another audible pattern.

Project Proposal

1. High-level Project Description

Acoustic systems of location and event identification have several applications in the everyday world, being present in security systems, earthquake recognition, sonar and various types of man-machine iteration.

Shooting sound mapping techniques began to be implemented in the last decades, even though it has been a problem of interest since the mid-First World War. In addition to military practices and environmental protection (e.g. detection of hunters in forbidden areas), this mechanism can be used in urban areas, providing instantaneous data to the local police or collecting data for further study of violence in certain areas.

Aiming to recognize and map specific types of sounds, an idea of an intelligent and self - adaptive system was developed based on the functioning and learning of the auditory system of some species of owls.

Owls are animals that possess a powerful hunting ability during the night, and to accomplish such a feat, as at night the sight is naturally more overshadowed by the absence of light, the owl has to use other benefits of evolution to improve accuracy of predicting the location of your dinner, one of them is the sound.

Experiments conducted by neurobiologists Eric I. Knudsen and Masakazu Konishi in Mechanisms of sound localization in the barn owl have been able to prove, using barn owls as the species of study, that this species of owl is able to locate a prey being immersed in a totally dark room, only using the sound emitted by its prey.


                                                                             A barn owl

The great evolutionary advantage present in this species is related to the considerable asymmetry that exists between their ears, it is known that the left ear is positioned around centimeters below the right ear, and with this difference of height, the owls can receive the information of the emitter with phase shift. From this difference, it becomes possible to accurately measure the location of its target, much like the triangulation positioning process, widely used in telecommunications engineering with telephone networks, or even in satellites. A very interesting video produced by the BBC demonstrates the whole hunting process of this species: How Does An Owl's Hearing Work?.

                                                           Front vision of barn owl skull


                                                            Back vision of barn owl skull

Owls that locate their prey using a sharp hearing aid are not born with this technique already welldeveloped, thus necessitating an apprenticeship to adapt to their own physical characteristics (skull diameter, height difference between the ears, etc.) that can vary significantly in the same species, beyond that, the owls have on the side of the head channels of rigid feathers that can regulate the passage of sound. Thus, these animals have a very efficient adaptive control, allowing that the accuracy in the location prediction maintains high even when dealing with different environmental conditions or physiological differences inherent to the species.

The technique of finding the coordinates of an unknown source from delays in reception
of the signal in receivers distributed in a known manner in space is part of a technique called
multilateration, which has no trivial solution. It is possible to show with algebra that in an N dimensional space N+1 receivers are needed,
with known positions, to uniquely determine the coordinates of an unknown source.


Taking a case of easier visualization, there are 3 known receptors R1, R2 and R3 and a target T with unknown location in an x-y plane.



When T emits a sound, the receivers detect the signal at different times. From the image below it is realized that R1 will receive the information first in a time t, R2 in t+dT1 and R3 in t+dT2.
To calculate the distance between T and the i-receptors we have:



Where v is the sound velocity and ti is the time of arrival of the signal from T to the i-th receptor



Centered at each of these receptors one can draw the circles C1, C2 and C3:



The only unknown variables to this system of equations are x, y, and d1. For purposes, it is possible to solve this system by applying direct minimization techniques, otherwise, in the real case, with noises and inaccuracies, these circles do not have a intersection and we need to define cost functions with numerical algorithms (e.g. gradient descendent) that minimizes the error and find and approximate value for T.

Bibliography

[1] J. Pak and J. W. Shin, "Sound Localization Based on Phase Difference Enhancement Using Deep Neural Networks"

[2] Renda, William & Zhang, Charlie. (2019). Comparative Analysis of Firearm Discharge Recorded by Gunshot Detection Technology and Calls for Service in Louisville.

[3] Mandal, Atri & Lopes, C.V. & Givargis, T & Haghighat, A & Jurdak, Raja & Baldi, Pierre. (2005). Beep: 3D indoor positioning using audible sound. 2005.

2. Block Diagram

                                                                  Block Diagram of iOwlT system

FPGA

The FPGA will have 2 essential modules, the Digital Filters module, that will proccess the sound digitalized by the A/D converter, using parameters adapted to the environment, and the Neural Network module, that has the neural network algorithm to identify the sound proccessed by the Digital Filters module.

A/D Converter

The DE-10 Nano board has an analog-to-digital converter of only one output, so the signals picked up by the sound detectors pass through a 8:1 multiplexer, observing the datasheet, it is noted that this mux takes a total of 3µs to switch, and therefore the largest possible delay in the acquisition of the signals, i.e considering 8 sound detectors, is 3x7 = 21µs, as the audible frequency is in the range of 20Hz to 20kHz, using the Nyquist theorem, the sampling rate for the set of observed signals is 40kHz, and this results in 25µs, so the analysis of the signals is practically simultaneous, leading to an increase of considerable performance as well.

HPS (ARM)

The HPS will have also 2 modules, the Adaptative Control module, that will change the filters parameters to adapt the solution to the environment, and the Multilateration Algorithm, which is the effective measurement of where is the sound emitter using the multilateration technique.

3. Intel FPGA virtues in Your Project

Adapt to changes

Processing of sound signals made in the FPGA is supported by adaptive filters, and therefore depending on the distance of the sound source, or the type of sound (being more general than gun shots), eventually will result in a change in the variables which determines the nature of the filter, variables such as bandwidth, cutoff frequency, etc. Therefore, the iOwlT system is adaptable to this feature.

The present project can be used not only embedded on a police car. Depending on the use of the technology, the iOwlT system may well be positioned in static strategic positions, such as on traffic lights, an interesting application would be to identify a possible earthquake imminence, since the onset of a seismic shake is determined much earlier by sound signals of high intensity but with very low frequency, being audible to animals like horses but not to humans. Such sonorous signals could be identified by the iOwlT system, and therefore there would be a longer preparation time for the coming earthquake.

Boost Perfomance

The iOwlT system, using FPGA technology, can performs the processing of a neural network much faster than a sequential technology for example. With the advantage of parallel processing, each neuron of an MLP can be computed simultaneously, and therefore it accelerates the recognition process of the received signal. As discussed in A/D converter section of Block Diagram, the almost simultaneity of signal analysis contributes to increase considerable performance as well.

Expands I/O

The analog inputs of the DE-10 Nano board will mostly be occupied by sound detectors, although 4 detectors would solve the problem of precisely determining the target, as a form of security, adding extra microphones does not increase the cost considerably and ensures the reliability of the signal that will be further processed.

The output of the neural network will result in the location of the sound event, such output will be sent by the Bluetooth module to the connected mobile phone, so that the location of the event can be shown in the application with the help of Google Maps API.

 

4. Design Introduction

5. Function Description

6. Performance Parameters

7. Design Architecture



10 Comments

Maria Dias
Thank you for this work! A great impact in Brazil's security is surely the key for our development!
🕒 Jul 07, 2019 11:51 AM
Arlene Haines
Congrats for such an inspiring project!
🕒 Jul 07, 2019 11:41 AM
Dr. Jason Thoreou
An interesting project!

Glad to see someone working at the intersection of wide variety of domains. All the best!
🕒 Jul 06, 2019 02:33 PM
AS026🗸
Thank you, Dr. Jason! This competition is a great opportunity to learn and to apply our knowledge to solve real problems, I'm curious to see your feedback to the final implementation.
🕒 Jul 07, 2019 08:39 AM
carlos silva
Even Bolsonaros' policys cut the support for reasearch you keeping believing in to do science in Brazil. Congrats!!!
🕒 Jul 05, 2019 10:10 AM
AS026🗸
Thank you, Carlos!
🕒 Jul 07, 2019 08:35 AM
Lucas matheus zirondi
Good luck! Wish you all the best!
🕒 Jul 01, 2019 11:40 PM
AS026🗸
Thank you, Lucas!
🕒 Jul 07, 2019 08:35 AM
Hygor Jardim da Silva
Excellent project, congratulations to those involved. It has enormous potential for several Brazilian regions and also for the world.
🕒 Jul 01, 2019 09:07 AM
AS026🗸
Thank you, Hygor! I really appreciate your words, it is such a pleasure to represent Brazil in this global competition, hope you will like the final implementation.
🕒 Jul 01, 2019 10:41 PM

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