My 9-5 is data and my 5-9 is music, so I love a good music x data mashup!
Thanks to the Insta algorithm, I scrolled upon an experiment that has become my new favorite in the world of music x data: Yuja Wang’s Rachmaninoff Marathon Experiment. "What happens when Rachmaninoff's music touches your heart?"
Pianist Yuja Wang joined The Philadelphia Orchestra and conductor Yannick Nézet-Séguin at Carnegie Hall for a Rachmaninoff marathon. Throughout the performance, Wang, Nézet-Séguin, members of the orchestra, and concertgoers wore devices to track their heartbeats.
The results are some of my favorite data visualizations! I encourage you to watch this 15-minute video showcasing the results. Or read this. You don't need to be a music or a data nerd to understand!
Props to whomever made these visualizations for using a step chart instead of a line graph. Step charts are the proper variation of a line graph to use when you are displaying data that was collected at a specific moment in time, the time between those moments might not be equal, and what happens between those moments is not certain. Since a line graph connects two points with a straight line, it can give the false impression that a location between those two points was a real data point when it was not, or give the false impression that the data is increasing or decreasing between two points due to the slope of the line. Step charts overcome this issue.
My favorite findings:
I was struck by the moments when Wang and Nézet-Séguin's heart rates so clearly diverged. As a musician, I know that moments challenging for the soloist may be easy for the conductor and orchestra, and vice versa. A prime example is a cadenza, a section of the music where the soloist plays challenging riffs on the main melody alone to show off. It is expected that Nézet-Séguin's heart rate dramatically drops, as he gets a brief reprieve on the podium, but it is telling how his heart rate still rises along with the music's intensity and everyone else!
Also striking was how different heart rates sustained or decreased during the finale. Everyone's heart rate rises together toward the emotional part of the finale, but the orchestra and audience's heart rates drop lower soon after. However, Nézet-Séguin and Wang's heart rates continue to stay elevated and sustain their higher BPM through the end of the piece. Interesting to see how the music has lasting effects on specific groups!
Inspired and intrigued, I downloaded my Oura ring data from a recent orchestra concert. This particular concert was a good one to use for a few reasons:
It was a chamber concert. Fewer players means more pressure on each player in the section and the smaller orchestra facilitates more connection across sections.
This concert was 100% music I really, really, really enjoyed. Our repertoire was Vivaldi's Four Seasons and Piazzola's Four Seasons. ***See relevant Tangent On The Topic of Music and Data below!
While I haven't tested this theory yet, I suspect I would have a higher and more reactive heart rate at this chamber concert than at a symphony concert where I'm part of a much larger section and not always as emotionally connected to the repertoire.
From these two visualizations in the app, I knew there would be some interesting findings around the time of the concert from 7:30-9:30pm.
Some challenges:
My Oura ring didn't capture my heart rate at a super-detailed level of time granularity. When I play, I move the ring to my right ring finger because it is too thick to comfortably keep on my left hand. Thinner fingers are not as accurate for the ring, and Oura recommends wearing it on a pointer or middle finger. The space between the sensors and my skin could have affected the volume and accuracy of the data collected during the performances.
I didn't have the timing data of when exactly we started the concert. I'm not sure that we started on time at 7:30 and several people made short speeches before we began to play. Additionally, I don't have the timing of each movement within each piece.
It was HOT! The performance venue was undergoing renovation, and the air conditioning wasn't completed. It was the first hot and muggy Midwest day of the summer, and our conductor decided to turn the ceiling fans off due to the noise they generated. That could have pushed my heart rate up higher overall.
Here is my detailed heart rate data on performance day:
To avoid disclosing too much of my personal routine publicly on the internet, I hid the precise time stamps but added contextual notes.
Step charts require major finagling in Excel, which I didn't have the energy to do for so many data points, so a line graph it is.
My observations: My heart rate was higher than expected while asleep. Commuting to rehearsal, rehearsing, and commuting back home is about 5.5 hours. As you can imagine, this is a big change in my evening routine, and I tend not to sleep as well during concert cycles. Oura's UI informed me that a higher heart rate indicates your body is stressed and trying to recover. Or maybe I was having bad dreams.
After waking up, my heart rate fluctuated within ~15 BPM throughout the day. Notably, I did not exercise during the day, as I typically skip workouts on performance days. I can't remember exactly what I did that day, but it probably included errands, housework, and relaxation.
My heart rate dipped and remained steadily low for the hour or so I commuting to the performance venue, when I was sitting and listening to classical music.
My heart rate rises once I am at the venue, warming up, and the performance begins. The volatility in this data section is interesting, as my BPM almost doubled and oscillated within up to 70 points.
Let's double-click on that:
The first half was Vivaldi's Four Seasons, a work I have loved since childhood, am super familiar with, and could play in my sleep. My heart rate actually trends lower as that performance continues. This section has much more oscillation, as the movements generally alternate between faster, exciting music and slower, calming music.
Based on the average length of Vivaldi's Four Seasons in the first half, I thought that the second big peak was the start of the second half after intermission, but once I added in approximate timestamps gathered from performance lengths averaged from YouTube, I changed my hypothesis. During intermission, I was rushing around (in heels!) up and down a flight of stairs and through crowds to drop my violin off in our Green Room, find a bathroom without too long of a line, retrieve my violin, and be back on stage in time. My heart rate was high while performing for emotional reasons; my heart rate was high during intermission for physical reasons.
Similar to the Rachmaninoff Marathon Experiment, my heart rate fluctuates throughout the performance but trends upwards toward the very end of the concert.
The second half was Piazzola's Four Seasons, a work I hadn't played before and, therefore, had to be more "on my toes" musically. It also included some fun, new tango techniques that required more thought to execute. That could have driven a higher average heart rate and increasing heart rate trend throughout the second half of the performance.
With both halves, my heart rate jumps dramatically in anticipation of the first downbeat!
My heart rate doesn't sustain lower numbers until I am driving home. On the way back, driving on dark backroads, I listen to the Top 40's radio to keep me awake! That lively music, post-concert adrenaline, and slamming the brakes for the occasional deer on the road likely kept my BPM ~20 points higher on the drive home than on the drive up.
It can be hard to unwind after a concert, but my heart rate decreases once I am back home and doing my bedtime routine.
My takeaways:
While I was not able to draw precise correlations between specific musical moments and my heart rate due to a lack of data granularity, the data shows that my heart rate rose, remained elevated, and fluctuated significantly during the performance time. This indicates that I was engaged in the performance, feeling the music... or at least feeling the pressure to play my best. This engagement continued post-performance, hinting at the music's lasting emotional effects!
***Relevant Tangent On The Topic of Music and Data:
The Uncertain Four Seasons is a masterpiece work that doesn't get performed or talked about enough. This newer composition re-imagines Vivaldi's classic Four Seasons in the future, using climate change data and climate change science. I am obsessed with this sonification of data concept and audible data story! I'd love to perform it one day!
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