Sonification and Life Forms II

Since excitedly sharing the results of a sonic analysis of Lemur Gut Microbiomes, I have been working up a soundscape based on the quick sketch included in the first Sonifications and Life Forms post. (You can hear both below.) In order to get feedback on the work, I sent the first blog post to Mark Ballora, with whom I had taken a data sonification workshop in May. His response helped me realize the need to clarify my sonification process. So here is a description of the project:

The purpose of the sonification is to illustrate the changes in baby lemur microbiomes from birth to weaning. Microbiome data was captured through fecal samples taken at birth, through nursing, introduction to solid foods, regular solid foods, and two times while the babies were weaning. The sonification will illustrate changes in the type and amount of bacterial phyla present at each of the six sampling stages for all three lemur babies. In addition, the mother’s microbiome was sampled at the time she gave birth, so her profile, which was assumed not to change, provides a baseline adult profile with which to compare the babies’ changes.

There were 255 strains of bacteria collected over the course of the study. These fell into 95 classes and 35 phylum. I focused on the phylum, as my plan was to assign a note value to each bacterial data point, so I needed a smaller data set. The data set was narrowed further (and made more interesting) by focusing on a family: a mother Pryxis, and her triplets, Carne, Puck and Titan. This group allows us to not only hear the variety of changes in the babies’ microbiomes, but compare the changes as well.

The original data set included 9 lemur babies and 7 mothers. So the first step was to go through the phylum data sheets and pull out the profiles for Pryxis, Carne, Puck and Titan. A phylum profile would be the type and amount of each phyla present at each data collection point. The profile changes over time at each collection point. The microbiomes of these four lemurs housed 15 phylum (at a density of >.001) out of the 35 found in the entire study group.

The next step was to assign a note value to each phyla. Since there are only 13 notes available in the chromatic scale, some phylum would need to be on the same note, albeit a different octave. Same note, different octave will lend a tonal consonance to the profiles. So what might this consonance represent? There were 5 phylum that had the greatest density and presence in all the samples, so I assigned those to the note G from octave 1 to 5. The remaining 10 phylum were assigned note values based on their presence throughout the profiles, and on their consonance/dissonance with the tonal center G.

In order to capture the density of each phyla, a midi velocity range was aligned with the decimal percentage of the phyla in each profile. Midi velocity settings determine the force with which the note is played. Thus the velocity ranges render a clear sense of presence or loudness to each note played. The decimal percentages ran from .001 to 1.0 and the midi velocity range runs from 1 – 127. Here is a chart of how these ranges overlap:

So for example, Protobacteria present at .25473 would be represented by the note G at octave 3 set at 40 velocity. The largest sample in all the data points captured for this project was around .9 and the smallest was .001 (this was a cutoff point as there were bacterial phylum present down to .0001 ranges.) Here is the chart for Titan showing note assignment and density values through each sample stage:

My sounding board for this data comparison is Ableton Live, a digital audio workstation (DAW). The individual lemurs are represented by a “voice”/midi instrument in Ableton. Tuck, Titan and Carne are bell-like voices that blend together, while Pryxis, the mother, is a warm, pervasive woodwind. She envelops and contains the changes in the babies’ phylum profiles.

All lemurs had a Phylum Profile Chart like the one above. In the DAW, the instrument track for Titan, for Puck and for Carne contains a midi-clip of notes of the phylum colonies present at each stage of dietary change, which were then laid out as a “scene” in Ableton. As example, Titan’s Phylum Profile at birth was

Protobacteria (Note value=G3) set at 101/127 in intensity

Euryarcheatae (Note Value=A2) set at 34/127

Firmicutes (Note Value=G2) set at 11/127

Cyanobacteria (Note Value=A#4) set at 1/127

Other Bacteria (Note Value=B3) set at 1/127

Spirochaetae (Note Value=G5) set at 1/127

Titan’s Birth Phylum Profile is the multi octave chord GAA#B. Three of the phylum were barely present, so those tones are almost inaudible in the chord. However, 2 Gs and the A ring out. The total number of phylum present in each dietary stage varied from 3 to 14, so the multi octave chord becomes more dense and dissonant when the phylum are so varied. Here is a look at the tracks (individual lemur voices) and the “scenes” (which are the phylum profiles from all 3 babies at each stage.)

The first sketch was just the mother’s phylum profile droning under the three babies’ profiles expressed as a stacked megachord. All 3 baby profiles rang out together four times at each stage, starting with birth and ending with the second wean. What could be heard was a homogeneity and consonance between the Mother and babies at birth that gradually became more diverse and dissonant as solid food was introduce. However, by the second wean, the babies’ and mother’s profiles become more consonant again. The researcher said this illustrated the conclusions of her study.

As a soundscape artist, I felt there was more here than just that basic chordal movement. The babies’ phylum profiles were quite different from each other as well, which is lost in the chord presentation. For example, Carne’s birth profile has only 3 phylum, while Titan has twice that amount. One way to hear this level of contrast in the baby profiles is to articulate the chords into riffs. Now we can hear the interplay of the changes in their microbiomes. In addition, we can hear how consonnant/dissonant and dense the phylum become as outside food is introduced into their systems. Titan’s phylum profiles arpeggiate down, Puck’s go up and Carne’s go down then up. A practiced deep listener could key in on a particular profile and follow it through to the end. I played around with rhythmic shifts to create more movement in the stages where the phylum profile were incredibly dense and diverse. The last two arpeggiating riffs you will hear are all of the phylum notes sounding through twice. And listen for the elevated levels of Protobacteria in all 3 profiles at birth – that G3 rings out at that point.

As I put this full family profile together, another more nuanced movement in the data appeared. In the chord rendering, I heard the data get more dissonant and dense from nursing through first wean, and then the phylum thinned out and became more consonant at the last wean. In the riff rendering, I can hear a contraction and more consonance at the Intro to Solid Foods stage as well as the second Wean. That was not clear in the chord presentation. When I checked my data records, there was a drop in the number of phylum present between Nurse and Intro stages. I love that a nuance appeared in the listening that made me go back and check the data. That is exactly how I hope this process will work.

Some other things for future consideration:

Aligning each phylum tone to a particular beat might help the listener hear the differences from stage to stage more clearly.

When assigning notes to data points, closer attention to the harmonic overtone series might help clarify the role consonance and dissonance play in hearing the data.

The voices of the baby profiles have similar timbre as a unifying element. The profiles could have very distinct voices which might make the variances in their profiles more audible.

Up next – Sourdough Songs.

Sonic Illustrations and Life Forms

Data sonification is a burdgeoning area of sound design that is quite amazing in its depth and flexibility. I have a keen interest to sonify data in a way that furthers our understanding of the data. I would love to create a sonic pie chart for example. While a visual pie chart is a snapshot, a sonic pie chart would be more like an animation. A chemical reaction could be sonified by assigning particular voices to different parameters of the reaction: as the reaction proceeds, the voices would change from “reagent” voices to “product” voices. Consonance and dissonance couid illustrate the changing relationships amongst the components of the chemical reaction. One possible way to sonify, in my mind.

Then at Moogfest 2018, a workshop introduced me to the world of SuperCollider and MaxMSP as instruments for creating sonic pie charts. Mark Ballora of Penn State University (Please check out his work at http://www.markballora.com) has been working with sonifying data for decades. He was doing it when no one was paying attention. Mark uses SuperCollider to create sonifications of tidal changes and the movement of hurricanes. This type of sonic representation of data illustrates how a group of parameters changes over time, and when you listen, you hear all of the changes happening over time. Voila! A sonic pie chart! Attending Mark’s workshop, shifted my soundsense, as I realized I do not want to learn computer programming (at this time). This blog post by Mark Ballaro and George Smoot (https://www.huffingtonpost.com/mark-ballora/sound-the-music-universe_b_2745188.html) helped me understand that my interest is in exploring how modal/timbral shifts that are set in a familiar,equal-tempered scale spectrum might illustrate data-driven relationships. What I am interested in is more a sonic illustration, than a map or a pie chart.

Just before Moogfest, The Dance DL, a Durham dance listserve sent this announcement:

Auditions & Open Calls

Arts & Sciences Collaboration: Sourdough Collective – Rob Dunn Lab

Where: AS IF Center in Penland, NC

Rob Dunn’s lab at NC State University explores microbiomes of some of our most familiar places. The sourdough project studies sourdough starters from around the world, including some really ancient ones that have been passed down for generations. Seeking an artist working in any media with an interest in microbiology, bread baking, making the invisible visible, and/or communicating complex science through art. Help us bring the awe and wonder of science–and the microbial world– to the world.

As I read this notice, it felt like a dream! I have a two and half year old sourdough starter which is used to create 75% of the bread Trudie and I eat. I have recently studied cell biology, neurobiology and have a deep interest in molecular chemistry about which I am just learning. And I am looking for a data sonification project. I sent them an inquiry, they checked out my sound work, and I was invited to participate.

First step, meet with the Sourdough folks at Rob Dunn’s Lab. On Friday June 15th, Erin McKenney, post-Doctoral Fellow in Microbiome Research and Education and a research lead on the sourdough project, and Lauren Nichols, Dunn Lab Manager, met me in the lobby of the David Clark Labs (home of the Dunn Lab). I learned that the sourdough project is looking at the ecology of sourdough starter communities as relates to yeast and bacteria growth in flour when exposed to water and the local microbial environment. I attended a lab staff meeting and learned about the amazing research being done here. All the projects are basically looking at how the smallest phenomena impact much larger phenomena and vice versa, the micro to macro to micro feedback loop. And they keep finding that diversity is the key to sustainable growth and a healthy environment. I left the meeting excited and inspired! Next stop will be the As If Center in Penland, NC in October.

The only other preparation I would like to do is to try sonifying some data. I reached out to the Rob Dunn Lab folks, and Erin McKenney sent me a data set to try my hand at. The data is about nine lemur babies from three lemur species, and how the microbial makeup in each baby’s stomach evolves as changes are introduced to their diets. (This is Erin’s dissertation study!) We have identifiable parameters that can be orchestrated to show changes over time. Perfect!

The data is on a massive (to me) spreadsheet with lots of terminology I don’t know…yet. This will be an interesting process as we work out exactly what the sonic map will depict. I sense that certain data will lend itself to sonification and that is the part I do not yet know. After spending some time studying the spreadsheet, I asked Erin how we can cluster some of the microbial data together, and she sent me the class and phylum data sheets. Phylum became my focus as there were only 35 phylum as opposed to 95 classes and 255 strains of bacteria. One of the lemur mothers had triplets so I decided to put together phylum profiles on this small group. Culling through the data for these specific individuals narrowed the phyla divisions down to 24, then I made an arbitrary cutoff point of >.00 density for each phylum (Erin said this was fine and is actually a tool scientists use to declutter data). Now was down to 15 phylum – a manageable number for a timbral illustration.

The microbes were collected from the three babies six times from birth to nine months. The timeline for the samples was birth, nursing, introductory solid foods, regular solid foods, and two times as they were weaning. Microbes were collected from the mother when she gave birth. Erin had the brilliant idea to have the mother’s phylum profile (which does not change over time) be a drone under the babies’ phylum profiles in the sound map. This allows you to hear when the profiles diverge and when they converge.

The sonic substance for all this is a phyla megachord that stretches from G1 to G5. Each phylum is voiced by a single pitch, so, for example, Protobacteria is G1. Since there are only thirteen pitches in a chromatic scale, some of the phyla would land on the same pitch, different octaves. There were five phylum that tended to have the highest presence in each sample, so I made them the Gs, and all the rest had separate, distinct pitches. I used amplitude to render the amount each phylum was present in each sample.

Then there was how to voice the individual profiles in order to hear the data as clearly as possible. After much experimentation the mother’s voice is a woodwind with steady tone throughout. I chose bell-like voices for the three lemur baby profiles, letting each phase ring out four times over the mother’s profile. The idea is to listen and compare the mother’s profile with the babies’ profiles. Listen for the change (or lack of change) as the each stage rings in four times. You will probably need to listen closely several times. What you hear is a uniformity of tone at birth that becomes more dense and dissonant as the phyla diversify with the babies’ diversifying diet. Then the final wean profiles settle into more consonance with the mother’s profile. So very interesting!

When I sent this to Erin, she said, “The patterns you’ve detected and sonified are exactly what I published.” Yes! This is the sketch I will use to create a soundscape of the Lemur Data. From this exercise, some tentative questions have emerged that will help when we start working on the sourdough project:

How is the data organized/catagorized?

What is being measured?

What are the signifigant changes and time frames within the data collection process?

What are the researchers interested in hearing from the data?

And this is just the beginning!