Exspiravit ex machina

Getting this started has proved more difficult than initially envisioned, who knows why. I say this because I have been completely overtaken by this work and the questions that have arisen from it so, naturally, writing about it should be easy, right?

Right.

Let's start with a little background (or perhaps quite a bit of it), when I accepted this internship at NPR, I had no idea, truly, what I was in for. I've spent the last two years immersed in digital humanities and librarianship, realizing that this space was perfect for me. It was a weird combination of all the things I love, addressing the many and myriad questions I had about being a scholar in the future whatever that means, and it allowed me to focus on the things at which I'm really quite good: workflows, information management, metadata, academic technology, and so on. These were things that, over the years, I've noticed musicology as a discipline had little interest in (something that did not and still makes no sense to me personally) and this outlet was one I needed badly. So when I saw the call for interns and read the description, I knew it was something I needed to do. I had no idea just how much that decision would change my life.

Well that was dramatic.

My internship began and I learned about my daily tasks, things I was aware of such as ingesting new promotional music into our in-house database. After a few weeks of general intern training and practice working with our systems, I moved on to the real meat: music information retrieval (MIR) mood tagging. Now I had to do some research, you know, real scholarly stuff first. I read published papers by those foremost in the field such as J Stephen Downie and read the documentation provided by Essentia, the algorithm library we use to assess our tracks. Scintillating stuff that lead to the deepest of rabbit warrens. It was here that I learned about digital musicology, a term I had heard but with which I had not engaged. I am still learning quite a bit about it but what I have gleaned so far is that…there are not a lot of musicologists involved in digital musicology. That might sound odd to you, it sounded odd to me at first. Let's spend a little time on that, shall we?

Digital musicology along with music information retrieval touches on a number of various fields: music theory, acoustics, music cognition/perception, music psychology, programming, music science, library studies, and more. I suggest the Frontiers in Digital Humanities page linked above for more information. And to be fair, there are musicologists engaged in this work, asking really interesting questions. But when MIR is put to work in the real world, say, music streaming services, the specific tools that musicologists have as humanists are shelved in favor of the tools of theorists and programmers. 

Enter yours truly.

In the process of undertaking a massive assessment of several MIR algorithms, I found myself asking lots of epistemological questions — humanist questions — that seemed unanswered. What is tonality and how do we define it for an algorithm? What should be included in our training models? What biases are represented and replicated in our algorithms? Musicologist Anna Kijas touches on these things beautifully in her Medium post, taken from her keynote at the very recent Music Encoding Conference (at which my project supervisor was present) and I highly suggest reading it. I will get into all of the problems I have faced and am facing but this is already quite long for a blog post. (I know, it's my blog, I can do what I like. Point taken.) Plus, I feel it's only fair to give those problems the space they deserve.