Billboard has kept a chart of their top 100 musicians since July 19, 2014, and like any of Billboard’s charts/lists/etc., the “Artist 100” attempts to measure, sanction, and archive the popularity or success of contemporary music in real time. Unfortunately, I can’t find any sort of announcement from Billboard outlining why the company instituted this new chart when it did, or what sorts of justification they used, and there’s no Billboard magazine from July 19, 2014, nor the week prior. An obvious difference between the Artist 100 and any of Billboard’s other charts is its focus on the musician rather than on a song or album. Continue reading
Well it’s been quite a while since I posted something, the reasons for which will constitute another post soon. Basically, the better part of the past year involved struggling through the uncertain, difficult precarity that is the academic job market while finishing the bulk of my dissertation. Anyone interested in the music theory job market in general should check out Prof. Megan Lavengood’s excellent post on her own experience with some nice overall stats, and Kris Shaffer’s post from a couple years ago. My experience in short: I was lucky, thankful, and extremely relieved to receive a 2-year visiting position after a grueling process that lasted from August until late April. I worry the market will be even worse during the next go around. Such is life in the academic rat race.
This post, though, is a brief summary of a side project I undertook smack in the middle of the job market process back in February. This exercise was basically a way for me to learn some new Python data management and graphing stuff, and was initially meant as a fun thing to do (i.e., procrastinate) to practice some programming. Essentially, I wanted to build a database of all songs to appear on the weekly Billboard Hot-100 charts, which I could then use to search for correlations or trends between genre tags, intertextual connections, or acoustic phenomena. I didn’t do much besides make some figures that track trends, but I’m hoping this can serve as the basis for a larger project. Continue reading
In the last installment of this #genre series, I laid out what I see as two competing narratives in popular music discourses: genre-is-dead and genres-are-(over)abundant. In this post, I’ll share one part of the results from my exploration of how these two ideas might coexist in some Spotify metadata. That is, I’ll show what kinds of genre labels Spotify gives to artists.
First, why focus on Spotify? It’s extremely popular, so it has a huge impact on how many people experience the ramifications of categorization. Second, they have an API that makes some of their metrics relatively easily accessible to the public.
Like any streaming service, Spotify has a bunch of ways they categorize their content and make recommendations. I’ll not get into their use of collaborative filtering, word2vec, or web scraping in this post, but you can check out this video if you want an idea of how some of this works. As Spotify gobbles up recommendation services, social media apps, database managers, and music AI companies, their black-box of categorization (and thus genre) becomes ever more opaque, its proprietary algorithms/data shrouded by their very heterogeneity. Continue reading
Before getting back to my #genre series, I wanted to jot down some thoughts I had after watching Baby Driver a couple months ago. Better late than never? I won’t make any big points about the movie since I’m unqualified to do so, but music’s centrality deserves a little reflection.
In short, I was sort of entertained but ultimately disappointed. I heard that music played an integral role, the main character (“Baby”) listened to his iPod a lot, and there were some sweet car chases, but that’s all I knew before heading to the theater. That’s basically what happens during the 112 minute runtime (which felt about 20 minutes too long), with some zany heists and gratuitous violence liberally sprinkled into a trite guy-finds-and-rescues-girl/muse narrative. Continue reading
After some recent twitter interest in my pop music analysis seminar, I’ve posted my syllabus with bibliography and schedule *here*. I’m also sending my syllabus to the wonderful curators of the SMT popular music interest group site. Take it as you will, and refer to my little post on music theory, citation, and gender for some relevant background. I’ve also changed my general approach to syllabi since this class, following my colleague’s adoption of more accessible resources. I think it’s important to at least consider the ideas on language, design, and format the authors of that site suggests. Continue reading
I’ve been busy with application materials and revisions of a largely unrelated article, so my dissertation work has lagged a bit this past month. Since it’s high time for me to write a post on my work, I figured this would be a good opportunity to oil up the gears again. This will end up being a multi-part entry spread over a few weeks, but I’d like to at least begin to share what I’ve been dealing with for the past semester or so. The current post is meant to set the scene of the project, so don’t expect many conclusions just yet.
Essentially, this series will run through the highlights from one of my chapters about how genre is used and experienced in popular music, broadly construed, during our current era of streaming and easy-access. One of my points in a larger project is to explore how ideas of pop categorization and genre signification have perhaps mutated throughout time; how do current understandings and experiences of genre differ from those during other tumultuous, rapidly shifting times in popular music? This particular chapter takes a synchronic slice of the pop pie, comparing some academic, amateur, and critical discourses from the past couple of years with a set of Spotify’s metadata from the spring of 2017. Continue reading