The Steelpan Vibrations Team thanks all the volunteers who contributed to this project since we have gotten many great classifications so far. We believe that you should get to know what this project is and how it came to be, so you can understand more of what we are doing.


The steelpan originated in Trinidad and Tobago and is essentially a 55-gallon barrel that was carefully hammered on the bottom for tuning. The unique nature of the Steelpan comes from the vibrations of different notes that are all coupled together since the notes are all embedded in the same piece of steel.

While we have a limited understanding of this vibrational coupling, we do know how the individual notes behave.

To understand how to interpret images made by electronic speckle pattern interferometry you can look for the concentric rings which indicate where the steelpan is vibrating. These sets of concentric rings are also known as antinode regions. The numbers or rings, or fringes, measures the amplitude of the note’s displacement.  The first image below shows a single note vibrating at the frequency of its first resonance (the lowest frequency resonance.) on a tenor steelpan.Vibration 1Vibration 2.jpg

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In the image above, we see the same note in its second resonance. This resonance has two antinode regions, separated by a nodal line running between them as seen in the image above.


It has been long suspected in the musical acoustics community that the sound of the steelpan has a time-dependent nature related to the transfer of energy between different components of the sound spectrum. It is not well understood how it works and historically has not been easy to observe the motion of the steelpan in the short time after a strike has occurred. In 2009, the Physics Department at Rollins College acquired a high-speed camera to use with their electronic speckle pattern interferometry system. Professor Morrison was intrigued by this and wanted to see if they would help him make those observations. When he went there, they were fortunate enough to help with his measurements.

The Caribbean Steelpans were chosen because Professor Morrison liked how it was a recently invented tuned instrument which is prominently used in the world. He also enjoys its deceptively complex mechanical system–meaning it looks simple enough as it is just a bunch of notes hammered into the bottom of an oil barrel, but the complexity of its rich sound grows deeper it is studied.

Through getting this research off the ground, there have been various failures. Such an early failure was that Professor Morrison looked exclusively at the first 100 or 200 frames after the mallet strike happens. He thought that was enough to analyze it, but he realized that he was looking for what happens in the first 2000 frames and would need a different approach. On the flip side, some successes were in the Summer of 2017 where the previous research team helped Professor Morrison develop this project into an official Zooniverse project. They chose Zooniverse since it was the best platform for a crowd-sourced volunteer to classify and analyze data that cannot be easily processed by software. A python code was written and developed last year, and they learned enough of the project to present their work to the acoustical society of America in December of 2017.


What we want to know is how do the vibrations propagate in different areas of the steelpan’s surface? It leads to a better understanding of how coupling vibrations may occur in other surfaces and how the mechanical energy is transferred.

That’s why we are asking you as a volunteer for help, so we can track the motion of vibrations as it travels through the steelpan when they develop and decay. So please, continue making classifications as it will continue to help our understanding of the vibrations!


Why we need so many classifications

In our last post, we showed some really promising average classifications – after removing obvious outliers.  In this post, we want to illustrate the reasons that we need such a high number of classifications before we retire an image.


The above image is an example of a frame where there is one antinode region in the upper left corner that has not been identified enough times to be included in the analysis. While it is possible that this antinode may not ultimately be important to our overall analysis, we would like to see as many of these marked as possible.  Our hope is that by getting more volunteers to classify this image, enough people would see that antinode to mark it with an accurate ellipse that we can include in an average.


The above image has two missing antinode regions – one is the strike note on the lower left side of the image where all the fringes have merged together and the other antinode is centered at position (200,150).  What has become obvious is that the most often missed antinode is the strike note. Often the vibration amplitude of the strike note is so high that there are no distinctly visible fringes. In those cases, the fringe count should be marked as 11, which is our way of saying “more than 10 fringes present”.  It is quite apparent to us that the strike note CAN be found by many of our volunteers, so we believe that having more classifications will allow for all the antinodes on all the frames to be marked.

Additionally, the average ellipse that we do see is possibly not the best representation for that particular antinode.  We are hopeful that with more classifications, the average ellipse would be a better representation.


In the above image, we are really happy to see the two antinodes identified by the classifications, but again, the strike note (on the left side) has not been marked enough times to be included in this analysis.


Here is an example of an image that has been seen at least 5 times, but there was not enough agreement on the position of the antinodes to include either of them in the analysis.

All of these frames are still in the project and waiting for more classifications to be made.  Thank you for all your help and please consider sharing our project with others!

A peak at the analysis we are doing

We wanted to take some time and update you all on what we are doing with all the classifications that our volunteers (that’s you) have been making.

Even though we only have one retired image in our project, we have been working on the analysis that we plan to do when the images are all retired. We have been able to identify individual antinode regions that you have marked with ellipses.  When we eliminate outliers from each cluster, we take the remaining ellipses and calculate an “average” ellipse.  Right now, we have done this analysis with images that have 6 or more classifications on them.

Here are some examples of what it looks like when we overlay the average ellipses on top of the original frames:


What we really like about these results is that our volunteers seem to be capable of identifying all of the antinode regions in the images and that the average ellipse can be a good representation of the antinodes.

The following two examples tell a similar tale, but with a few subtle differences.


The above image shows a note centered at (290, 50) which is vibrating primarily with second harmonic motion, as evidenced by the two antinode regions.  While the classifications made do pick out the two antinodes on this note, the average ellipse does not represent the antinode region as well as what we see in the top example on this page.


The last example, above, shows that the strike note is identified by our volunteers – it is the largest ellipse on the left side of the figure.  However, the average ellipse is slightly larger than what the actual antinode region is, as can be clearly seen on the lower right side of that antinode region.

What we really want you all to see is that your effort to help out with this project is absolutely paying off!  We are getting the information that we hoped we would get – thank you for your hard work and please keep it up!

Newsletter #2, June 2018

Hello Steelpan Vibrations Volunteers,

We are pleased to keep you updated on our Steelpan Vibrations project for the second week in a row.


We have updated our tutorial to make it more concise and easier to understand. We plan to update our Field Guide, so keep marking your favorite images and tag them so we can include them in the new Field Guide.

Last week we discovered problems with not being able to resize the ellipses when making a classification. It had been brought to our attention Wednesday afternoon and we immediately contacted the Zooniverse team. They fixed the issue on Friday. As of right now, the resizing of ellipses is working correctly. We apologize for any inconvenience.

On the flip side, our Facebook page is now active! You can follow it, give it a like, and share the page here:


We are still trying to reach our goal of 100,000 classifications by the end of the summer. We know that our technical error may have slowed down the process of you completing classifications last week, but we are back on track now. Please continue to reach out to family members and friends about our project and share it on social media.

As always, we thank you for being a volunteer. We can’t do this without you!

Keep helping at

Andrew & the Steelpan Vibrations Team

Reaching our Summer Goals: What will it take?

In our previous newsletter, we have discussed our goal of reaching 100,000 classifications by the end of this summer session, which has 7 more weeks to go!


Considering we have 26,167 classifications (as of writing this blog post), we have about 1/4 of our goal to reach so far. This means that we need 73,833 more classifications to go.


With 7 more weeks to go, we need about 10,548 classifications per week to reach our goal.
Within the first week, we received 199 classifications, on the second week we received 417 classifications. You can view this in Figure 1.

fig 1

Fig 1
“This publication uses data generated via the platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation.”


If we look at the week of August 21, 2018, the week we officially launched this project to Zooniverse, we received 4249 classifications. Since then, that had a general trend downwards until a sudden burst on November 27, 2018, where we received 3,098 classifications. The previous week was only a mere 457 classifications, so that’s a huge spike within the matter of one week.

fig 2.png

Fig 2
“This publication uses data generated via the platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation.”

The amount of volunteers who have participated in this project is 1,674. If each of those volunteers will make a classification every day, then that is 11,718 classifications per week. That is more than the amount we have for our goal. We are hoping that all of our volunteers participate every day and we are continuing to update each and every one of you per week with a newsletter of our progress.


At first glance, our maximum per week is only 4,000 classifications, which is way below our weekly goal. However, looking at another project (Planetary Response Network and Rescue Global Caribbean Storms) that has successfully finished, we can see that it received more than 30,000 classifications in a week!

fig 3

Fig 3 (

The difference in that project is that it contains a humanitarian aspect that ours seems to miss. Their title page boldly states “Join the Relief effort to help victims of Hurricanes Irma and Maria.” We can only speculate that title makes the volunteer seem empowered and willing to spend their free time to help out a humanitarian cause. Looking at this project in another direction, the classification project is just about as complex, if not more, than ours which requires the volunteer to identify images and the possible objects within.

Another project by the name of “Gravity Spy“, which does not sound as humanitarian as the previous project. The volunteer is told to identify an image(s) (varying in the scale of the graph’s axis). Classifying this project has a much simpler project as the volunteer to told to look at an image(s) and to simply identify it with the images on the right-hand side as seen in Figure 4.

fig 4

Figure 4 (
“This publication uses data generated via the platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation.”

It has 2 fewer steps than our Steelpan Vibrations project has, making it quicker to get a classification. Its statistics contain classifications ranging from 9,603 to 26,894. within the past year as seen in Figure 5.

fig 5
Figure 5 (
“This publication uses data generated via the platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation.”



From these two projects we have looked at, we can say that the interest from volunteers giving us classifications comes from a mix of ease to classify and a humanitarian effort that may tug at a volunteer’s heartstrings. While the numbers say is it possible to gain 10,548 classifications per week from Zooniverse’s dedicated community, we might have a few obstacles going against our project with how it is structured and the nature of it’s some-what difficult classifications project.

For now, we can say that it is definitely possible to receive at least 3,000 classifications per week, which might extend our goal in achieving 100,000 classifications to 24 weeks. Again, the Steelpan Vibrations team is hoping to achieve that goal in 7 weeks if possible. We will continue this by spreading the word on our project and possibly by simplifying the process if possible.

As always, we value you as a volunteer for Steelpan Vibrations and we encourage you to get classifications and recruit others to give us classifications!

-Steelpan Vibrations Team



Newsletter #1, June 2018

Yesterday we sent out an email newsletter to all our volunteers.  Here is that newsletter:

Hello, fellow Zooniverse members,

The team working on the Steelpan Vibrations has started to pick up this project again and we are happy to keep you all updated.  A biography of each of the team members can be found on the blog.  Now let’s get on to the details.


We are still collecting classifications of our high-speed electronic speckle pattern interferometry images.  So far, we have around 25,000 classifications from the Zooniverse community, but we have set a goal to get up to 100,000 by the end of the summer!  The team plans to get this done by utilizing social media in an attempt to reach a wider audience.

You might be asking: What can I do to help?  One big goal we set for ourselves is to get more people interested in our project.  So, tell your family, your friends, anyone you can think of about our page on Zooniverse as well as other Zooniverse projects to get them more involved with this online community.  This tactic can improve not only our project but many others.


We have recently taken an interest in a machine-learning algorithm that will make use of your classifications.  Be looking for more information on this coming over the summer.

We are also in the process of recruiting talk moderators.  We have messaged the most active people on our project and asked them to moderate our talk pages.  If you have an interest, feel free to message us and we will happily consider your role as a moderator.

That is all the news we have for now.

Thank you for being a volunteer for Steelpan Vibrations!

Summer research team introductions – Part 2

Hello.  My name is Keanu Vasquez and I am currently interning under Professor Morrison for the Steel Pan Vibrations project.  I am currently in pursuit of an electrical engineering degree which may sound a bit off for this research project, but I personally enjoy learning and engaging in just about any topic in the field of physics. For this steelpan vibrations project, I am very interested in having a Machine Learning AI to analyze our data and having the volunteers look at this.  That way, our data can be collected even faster, and, in the process, we can test how effecting using an AI is to analyze our data.  As of right now, I oversee the promotion of the project to get more of the Zooniverse community and even more communities to help us out with classifying our images.  I look forward to working on this project this summer and I hope that I can engage the team and the community to successfully further progress in this project!

Summer research team introductions – Part 1

My name is Matthew Lange and I am a student at Joliet Junior College, with an interest in physics and mathematics. I am majoring in Computer Science, and I plan to transfer to the University of Illinois at Chicago next spring. In my free time, I like to play computer games, but I also like to play guitar. I believe that this project will afford me the ability to strengthen my programming skills, and to continue learning about the vast world of physics. I look forward to working on this project with everyone over the Summer!