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Prototyping our Low-Cost, Songbird Identifier

Hey, Zach here with another songbird identifier update! Since the last post, I have been busy testing the prototype device by taking bird recordings in various locations. After this week I will be taking a short break before resuming work on the project with the rest of the songbird team in the coming semester. Right now we are primarily planning for the next steps in the development processes that we will begin in September.

Laser cut songbirds are much easier to catch…

Our first goal is to add mobile internet access to the device so bird recordings can be automatically uploaded to our database as they are recorded. The ultimate goal is to design the device that is easy to set up and deploy, at which point it will automatically begin recording and sending data to our website and database where the recordings, geographical location, and classification data can be easily viewed by anyone. We’re looking for a wireless chip currently. These are pretty cool, if you’re unfamiliar, you can connect DIY devices to the internet via a cellular provider. You just need to buy a data plan and set up a SIM card, then your device can connect to a 4G network and send data wirelessly!

The second goal is to make the device autonomous enough that it can run this way for at least a week at a time without intervention. In order to do this, we must create some sort of weather proof housing for the device so the device can be placed anywhere.We also need to have a power source that can allow the device to run for at least a week continuously while keeping the cost of the entire system fairly cheap. This may involve a rechargeable battery pack and/or some sort of solar charger.

Two of our current prototypes.

Now that we’re beginning to actually build our prototypes, it is helpful to begin looking at other, commercial varieties…

The “Wildlife Acoustics Song Meter” is commercial wildlife audio recorder. Running around $1000, without software, it is a prohibitively expensive option for schools, students, or any sort of mass deployment.

The guy she says not to worry about…

 

The weatherproof housing on the commercial device is nice, and it features weatherproof microphones, which don’t need to be an expensive feature. Additionally, this device runs for up to 400 hours continuously (using 4 D-Cell batteries) and features a “sleep mode,” so it only records when it hears noise, and a recording scheduler, so that you can control what time during the day it takes recordings.

Looking at expensive options like this is encouraging, in a way. So far we have a prototype device which achieves almost the exact same results, just in a less durable package. When we’ve got the whole team back working on this project this upcoming semester, I think we can finalize a low-cost, web-connected, enclosed prototype which will be ready for long term testing and deployment.

Then we can focus on the exciting work, the signal classification and database so we can identify what songbirds the device is hearing and where in the country they are!

We’ll keep you updated over the coming months, for now, it’s time to enjoy my few weeks of summer before I’m back to school and we start back up with this project.  


The 2017 Summer Fellowship Concludes

Over 11 sunny Ann Arbor weeks, our research fellows worked hard to answer their research questions. They developed novel methodologies, programmed complex computer vision and data processing systems, and compiled their experimental data for poster, and perhaps even journal, publication. But, alas and alack… all good things must come to an end. Fortunately, in research, the end of one project is often the beginning of the next!

Some of the fellows intend to continue working with on the research they began here while they’re away and many of these projects will be continued next summer! Definitely expect to hear updates from Nathan’s EEG Visual Decoding project and Joud’s Sleep Memory project. Additionally, two of the projects will continue throughout the next few months: Zach’s Songbird Identification and Shreya’s Electric Fish Detector projects will continue through to December!

Meet the Fellows, See the Projects

The fellows are off to a great start! Check out their blog posts introducing their projects:

Progress

 The team has been working hard to bring their projects to life. Check out these blog posts on their rig construction and data collection efforts!

Conclusions

Our fellows experience the peaks and valleys of research this summer, but they all came out on top! Check out their final posts for their results, posters, and other details!

Continuations…

A few of our fellows are staying on throughout this next semester for longer term development projects! Zach is going to be back to working with his team on the Songbird Identification Device project, and Shreya will be working through to December on the Electric Fish Detector project. Expect updates on their progress from them soon!


Octopus Wrestling and Computer Vision

 

Hello again my faithful viewers, and thanks for tuning in for another exciting octopus themed blog post. As always I am your host Ilya Chugunov, but today I’ve come with sad news; all good things must come to an end, and this marks the end of my summer research here with Backyard Brains. Now’s a time to grab a hot cocoa and reminisce on what we’ve learned and talk about what there’s still left to do.

First and foremost, if you haven’t already had the chance to look at my previous blog posts, you can see them here:

Octopus Learning and Behavior

Studying Aggressive Behavior in Octopodes

Now let’s recap and break this down into some conversational dialogue.

First, we found out, rather accidentally, that if left together our Bimacs will wrestle each other to assert dominance. This gave us the idea of using computer vision to gather data for analysis with the hope that we could identify some interesting features within their behaviors.

First I built my acrylic setup to record the octopuses doing their thing, making sure to have even lighting and a stable setup for my Go Pro so that the code didn’t just explode from all the variability.


The first, and most classic, behavior found in our trials with the Bimacs were “bouts”, which were little sumo-wrestling fights where each octopus tried to push the other around as far as possible; these were common when both the octopuses were excited and lasted about 5 seconds each.

The second curious behavior found was the “poke”, where one octopus wanted to provoke a real fight, but the other just wasn’t feeling it. The more excited octopus would waltz up to the lazy-bones and just briefly tap him with an arm before jetting off across the chamber.

I noticed that in both the bouts and the pokes, right as the distance between the two octopuses closed, and they made contact, the angle between them rapidly decreased too. They would approach each other sideways (almost backwards at times), then rapidly spin around right as they got close to poke/fight. In the poke behavior, the offending octopus would then spin back around and jet off, while in the bout behavior they’d just stay locked face to face.

Another notable thing our octopus do in their fighting ritual is change colors. As I assume you already know, these guys are covered in chromatophores and seem to flash bright black as they go on the offensive (Can you tell who the attacker is in that picture above?).

The poke behaviour elicited the same response, twice! The first bump was the attacked octopus darkening as the poking octopus approached it and second was the poking octopus turning a dark brown as he squirted away.

“But Ilya, how in the world do you process so much video? And how do you know when the fight starts in the first place?”
Why thanks for the question, hypothetical reader. I use a mess of MOG (Mixture of Gaussians) background subtraction, erosion, and band-pass filtering combined with the OpenCV convex hull functions to find the general outlines of the octopus, and then I check if they’re two separate blobs, or one combined megaoctopus. If they’re 2 blobs, they’re not in contact, and vice versa, so now it’s easy to define first contact and a bout vs a poke (long contact, short contact).

 

Using a simple windowing function and a pretty boring logistic regression, we can take a bunch of our video clips of octopus fights where we’ve already classified when a fight occurs, and from them predict a point of contact in a new video we feed into the algorithm. This is where the concept of machine learning starts to play into the project, letting a program learn from previous octopus video to predict what will happen in new octopus video.

I’ve compiled my research results and created a poster which I presented at a University of Michigan symposium.

What’s next?

For me, Canada. Heading up to Montreal next week.

In general, my code is up on my GitHub and is completely open source, so anyone is welcome to make changes to it, take it in whatever directions they want; you don’t even have to use it on octopus if you don’t want. 

Now for some musings…

I’m excited about computer vision. Historically, behavioral studies involve a lot of humans watching animals, recording specific events (like eating a certain food and when), or interpreting their behavior. This is not only time consuming, but also unscientific. In these studies, there needs to be redundancy. Multiple people need to record the events. Then, that data needs to be interpreted statistically to ensure that, on average, the interpretations are consistent between different observers. As you can see already, it is challenging. Computer Vision programs are changing this!
 
By taking the humans out of the equation, you remove chances for bias, for missed behaviors or interaction, or fudged results. Computer vision techniques can be used to comb over hundreds of hours of video footage, quickly providing researchers with quantifiable results. There are certainly still some behavioral studies that require human discretion, for instance, was a touch affectionate or aggressive, but for many researchers, computer vision is the future.

I think there’s a lot still to be done with computer vision and behavioral analysis, and this summer research was just me dipping my foot into the pool. There is much more data we can draw from the same video I was working with, tentacle position and length, how curled the octopus’s arms were, maybe even their heartrate could be extrapolated with enough clever coding. As I continue onward in whatever field of STEM I find myself in next, I hope to keep throwing computational power at problems that don’t seem like they even need a computer, because who knows, maybe they do.

I’ll leave you with some boring philosophy. No one, not a single scientist, knows for certain what the next big thing is going to be. No one knows when or where the next technological revolution is going to be, no one knows if the next world-changing invention is going to be made in a million dollar Elon Musk laboratory, or at 3am by a hungover student in their dorm room. So just know that when you read a blog post like this, about an 11 week undergrad project, even it has the chance to be something big; not all scientific breakthroughs are made by bearded dudes in lab coats, they could be made by you.