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  • The aim of this studio was to represent data in a unique, interesting way. Kate and I loved the idea of using the massive amount of Hubway user information (a bike sharing program in Boston) available publicly. At first, a basic bicycle likely doesn’t seem that interesting to most of us. That is what served as our inspiration and our challenge for this project-we wanted to give a certain kind of humanity to three randomly-chosen bikes. We tracked two data points-the time of day the bike was used, and the duration of the ride-of these bikes for a month. Then, we assigned them a personality, which would be our main guide later for creating the music. (The three personalities were depressed/lonely, a busy, and a cheerful) We used Max MSP to turn the data points into sound information, and Ableton Live to turn the sound information into actual music. Our final, fascinating result for this project are the three ballads of the bikes.

  • Our studio was Musical Typography, where we essentially created music for specific geography.  We started off the studio by looking at projects that use live data, how they portray it, and how we might represent that with music. Once inspired to create our own projects then broke off into groups to decide what kind of data we wanted to use to make music.

    We made a list of different data that we could access. We sorted each kind of data into two basic categories, data that is live and changes all the time, and data that is set and doesn’t change. Some of the data we were interested in exploring had to do with crime rates, stars, dogs, earthquakes and the Hubway system. We heavily explored stars and earthquakes. We were wondering if we could turn stars into music, and for instance what a constellation might sound like? We ended up choosing a project much closer to home. We chose the Hubway system.

    The Hubway is Boston’s bike sharing program. All of its data is open to the public. We decided to take this data and track each bike individually. We wanted to personify the bike and really see how it spends its day and where it goes. We chose three different bikes to track. We are using the data for the bikes for a whole month and comparing their journey. We originally planed on having two separate tracking systems, each a year apart. We decided against that later on though to keep the project simple.

    The Hubway gives us a lot of data on each bike. The information that’s important to our project is the duration of the bike ride, the start date, the start station and end station and weather the rider is male or female.

    We plan on having a circular visualization. Each Hubway station will be evenly spaced around the circle. Each bike will be a dot and it will go from station to station inside the circle and will leave a trail behind that will look like a spoke on the circle, turning it into a wheel. As each the bike makes more and more trips, the previous trips will become more opaque, creating a web of spokes. The three bikes we track will each be a slightly different color, and it will be fun to watch them shoot around from station to station.

    With the plan in place, we then spent the day going though all of the data we have and sorting though it. We have three years worth of data and it is very overwhelming. I think we successfully combed the data and have just what we need now. We can now bring it into Max and start analyzing it and turning it into music and visuals.

    The Hubway data is very difficult to organize. There is so much data that it is really difficult to sort through. The computer simply can’t handle all of the data at once, so we waited a lot for the computer today. We also decided to use different data. We are going to map the time of day with the volume of the music, and the duration of the trip with both the duration of the notes and the pitches for the music.

    We had another slight change of plans, and decided that we wanted to create an individual song for each of the three bikes, as opposed to one really long song. Each bike will have its own song and then depending on how the songs work together, we are going to layer each bike song on top of one another. We want to explore the concept of layering the bikes, and seeing what emotion that provokes.

    Since one of our main goals is to personify the bikes, we are going to use the music to emphasize that. Maybe one bike will be in a minor key, because maybe it doesn’t get used as much, or one bike might be super low sounding? It’s important to us that the bikes to sound different and take on their own personalities.

    Once we finished sorting through the data we put it into Max. We got a song from the data for each bike in Max. When we put all three bikes together it sounded horrible though. We hadn’t thought to keep the same musical mode for each bike, so none of the sounds matched. We then re-did the Max portion of the bikes, but kept the same settings for all of them. This made them much more cohesive. Once we had the midi file from Max for all three bikes, we brought it into Ableton. Max saves the file as a midi file, which means that it saves all the data about the music, except for the actual sound of the notes. In Ableton we were able to assign different instruments to each bike and start hearing what they sounded like together. One thing we found is that to have the bikes going all at once is simply too much sound. It broke up the sound better when we split up each bike into a bass, alto and treble range. We are just starting in on the music aspect, and have lots of exploring to do! 

    At this point we were feeling a little directionless and needed a reality check. We had made the music and had used the data but didn’t know what to do next. Our music didn’t sound particularly good, but nevertheless, it was made. We didn’t know exactly how to proceed. After expressing this to the coaches, we decided to start over. 

    The reason our music didn’t sound good the first time was because our data had no space. This time instead of layering the tracks on top of each other, we decided that each bike would have an individual track and song, but still the composition was a constant string of notes with no breathing room to allow any thought to develop. We had also lost the concept of the personifying the bikes somewhere along the way of our process, and wanted to get back to that idea.

    One of the ways we addressed the thickness of sound was to add space to the data. For instance, after a really long bike ride (duration) we would then add a musical rest for each bike that was the same duration of the ride. We also came up with three different bike personalities: cheerful, sad and alone, and busy. We then added more musical space to match each personality. The sad and alone bike has the most space in the data and is in a minor key. The busy bike has no space of the data and is stressed sounding, and the cheerful bike has a few rests and is in a major key. We did all the Max data today and got the midi file for each bike.

    My partner, Julia, formulated the final music and I made the diagrams to explain our concepts. It was frustrating because I had to make diagrams for every single step of the process, which is a lot of diagrams. While diagrams are not fun to make or satisfying once you’re done with them, I can see that they are helpful in conveying your process to other people.

    I made three diagrams today explaining how we found the pitch, volume, and duration for the music. As evident from the diagrams, there are so many steps to getting ready to make the music then the actual making of the music goes by really fast and is completely an individual thing.

    This studio was less about the final project and more about learning the software. I’m much more adept at Max MSP and Ableton now, and glad for it.