Validating Citizen Science for our Research: We’re in the Right Place

We recently got the chance to share with the Acoustical Society of America how our volunteers are doing and answering the question: can citizen science effectively help us in our research?

We took some of the subject frames that have almost been completed. The classifications that our volunteers provided were all averaged to synthesize the completed frame. These aggregated frames are later strung together to make the desired mapping of the vibrating steelpan. To test if these results were sufficient, they were compared to the same frame but completed by the research team. Here is what the side by side comparison looks like.

The ellipses here are the indicated antinode regions and in the center of these ellipses is the averaged number of frames. The left column is the aggregated frames using the classifications from the volunteers and the right column is the research team’s answer for the same frame. As you can see the location and size of these antinodes are almost identical, and the reported fringe count is satisfactory between the volunteers and research team.

These similarities suggest that the citizen science approach is an accurate way to analyze the data. So keep up this great work everybody and invite some friends because it is only a matter of time until we can make some interesting discoveries together.


Steelpan Vibrations’ Full Launch

Great News!

We are proud to announce that Steelpan Vibrations has reached a full launch and is now an official Zooniverse project.  Please continue to provide feedback via the project’s page or through our blog. Also, come back here for any updates on our results that come from all of you!

Many thanks to our volunteers and the Zooniverse team. Keep up the great work!

Steelpan Vibrations Team

Looking at Antinode Size and Clustering

We have already commented on the accuracy of center agreement among volunteers when looking at all the classifications through the entire time interval. Now we can see how our volunteers match up for a single frame. Five sample frames were pulled out and classified by about 10 individuals.

The centers of these classifications were visualized and passed by the clustering algorithm that every frame will go through. These centers and clusters for one of the sample frames are given below (the black dots for the clusters refer to noise).










What was also interesting to look at were all the drawn ellipses for a frame. It can be seen that the points that are considered noise also deviate considerably in size along with distance from neighboring centers, which is common among other frames as well. The comparable size and orientation of a certain antinode region among volunteers now opens the possibility to effectively analyze these attributes throughout the time interval. As we gain more classifications, a more coherent picture of what each antinode region looks like through time will be made available! More ellipse plots can be seen below and keep up the great work everybody!

Spotify Playlist Now Available!

Our volunteers have requested in the past for the project to include some type of audio. Unfortunately, the audio data from the investigation of the steelpan are terribly boring to listen to since they are just single strikes. Some feedback mentioned music to accompany classifications. So, we have started a Spotify playlist for volunteers to work on classifications and listen to some steelpan music! We hope you are as excited about learning more about the steelpan as we are and contact us with any suggestions for the playlist or other comments/suggestions.

Finding the Antinode’s Center

Classifications are looking good! It seems that our volunteers have been successful in finding the centers of the antinode regions. Using the data export from the completed classifications and MatPlotLib, the centers of the identified antinode regions were plotted for each subject set. Those plots, along with a sample frame from the corresponding subject set are shown below. The tight grouping means that our volunteers are doing will with locating and indicating the antinodes’ position. Accuracy is also indicative by the similarities between the plot and sample frame.

Keep up the great work everyone!

Welcome to Steelpan Vibrations

In this Zooniverse project, we are asking you to help us map the vibrations caused by single strikes of a steelpan. We utilized Electronic Speckle Pattern Interferometry (or ESPI) and a high-speed camera set at 30,000 frames per second to acquire video data, represented by this video. What we are looking for in these frames are when the areas of maximum vibrations form and what is their amplitude through time. The areas of maximum vibrations, or antinodes, are indicated by the circular or elliptical regions that contain concentric rings. The amplitude is correlated to the number of rings, or fringes, each region encompasses, since the image works as a contour diagram.

Once we have the vibrations mapped, the video and audio data can then be
appropriately correlated. To your right, you can find some of the Amplitude vs. Time
graphs for the first three harmonics of the strikes examined in the project. When explaining this profile of the strike, we are interested in how all the coupled vibrations that we see on the surface contribute to each harmonic. We can then quantify the unique timbre of the steelpan and, possibly, better understand how coupled resonances
propagate in other surfaces.

To help with our project or to learn more, head over to our Zooniverse page!