For my masters year, half the marks came from one module, the masters project. Being a team effort, we were in a group of three. Putting our heads together, and taking ideas from lecturers, we made a list of potential projects. We knew for one that I wanted to be making hardware, and the other two wanted to use/learn machine learning and maybe FPGA’s. After much deliberation we decided to make a project that listened for a sound, and using time difference of arrival worked out where the sound came from. This post is mostly about the hardware and circuitry designed for the project.
With a world with a big focus on safety in public places, we thought it would be a good product for the security industry, potentially with links to smart cities. Imagine a shopping center, somewhere with lots of security already. They tend to have lots of security cameras, alarm systems and a dedicated guard. This isn’t uncommon in big public places/attractions, especially in the UK. Sports stadiums, train stations and museums are always looking for new ways to protect themselves and isolate problems. The example that inspired us was the horrendous shooting in Las Vegas at a concert in October 2017, just as we were picking projects. The main problem was that the security services did not know where the shooter was, meaning it took longer to get to him. If they had a system like we envisaged, the microphones would pick up the sound and triangulate it. The location could then be sent to relevant authorities to use.
To start with we needed microphones. We didn’t need to reinvent the wheel, and microphones can be easily bought off the shelf. For ease we used standard stage microphones, that had 3-pin XLR outputs. Although we had been warned that they would not work they had a good omnidirectional pattern, and had lots of good documentation. One issue with them is the output is balanced, which means it needs to go through a pre-amp. To get an idea of what a balanced signal is, imagine a ground connection and two signals. The two signals are the same, but one is inverted. This means when it travels down the cable it is much less susceptible to interference. This is part of the reason we liked using stage rated equipment, as sound engineers have already worked out issues with transporting sound signals long distances through noisy situations. We concluded from research that the signals could reach over 100m, which was the number we were aiming for.
Once the signal got to the box it needed to be converted to a signal that could be read by an ADC. To do this we used an INA217, a pre-amp designed for basically this purpose. An instrument amplifier, it measures the difference between the signals and amplifies them, outputting a voltage with reference to ground. The signal from the microphone is tiny, in the tens of milivolts range, so it needed some dramatic amplification to get it near the 5V ADC. The INA217 did a good job but we put a second stage amplifier to give it the extra push, as very large gains can sometimes be bad for a number of reasons. We used an OP07D but if we were to do it again we would get a rail-to-rail to get better results. This amp had a pot as one of the gain resistors so that we could easily trim the gain depending on test. Finally, the signal at this point sat between -2.5V and +2.5V so we needed to shift it up so it was between 0 and 5V. This was done with a simple shift circuit and an amplifier. We used another OP07D to make buying easier.
From here the signal gets read by the 12 bit ADC in an STM32 microcontroller. It then streams the data via the USB to a PC where MATLAB picks it up. This is where my knowledge is a bit lacking as I did not make it. In essence MATLAB uses a machine learning algorithm that had listened to over 1000 gunshots, screams and explosions. It has categorized them, and used a number of features to notice the difference. Then when playing a new sound of one of these things (not heard by it before) it categorizes it and outputs it to the user. It also used a selection of sounds from the background to know when there is not one of these events happening, else there will false negatives.
All in all the project did actually work. It detected gunshots and screams being played into the microphone, and the triangulation algorithm worked, just not in real time. We managed to win the best masters project, mainly because we had good quality hardware, a partially working system and a good business case behind it. There is a lot of scope of where this project could go, and many things that could be improved, but we were happy with how it came out. I may be able to use some of the circuitry on other projects, who knows. If you are interested in more of the project, maybe some more detail about the hardware or manufacture, comment or message on Twitter. Thanks for reading.