My first BOOK TALK! - Why You Like It, by Nolan Gasser
Welcome to WEEK 252 of MUSIC is not a GENRE (Video #78 & S4Ep15)
Why You Like It: The Science & Culture of Musical Taste – I Know Why I Like It! | MinaG Book Talk #1
And now for something completely different from MUSIC is not a GENRE … an entire episode dedicated to a BOOK! This week I discuss Nolan Gasser’s Why You Like It: The Science and Culture of Musical Taste. Find a comfy seat, because it’s a big one.
First of all, I LOVE that this book exists. I love that someone cares enough about music to write a 600+ page book just about LISTENING to it. The two biggest impressions you’ll get from reading this are: A. Nolan knows his stuff inside & out; and B. He realllly loves music. It would be an understatement to say this book is comprehensive, thorough, broad & deep. Nolan is voracious for music of all kinds, and reading this book will make you hungry too.
As you know, I appreciate anyone – artist or fan – whose musical tastes veer far & wide, who don’t pigeonhole themselves into one or a small handful of artists or styles. I’ve read other very well done books on more specific topics (I’ll discuss those in future episodes) that are a little myopic & insular – i.e. they’re such insider books that the author doesn’t spend a lot of time (if any) connecting that music to the rest of the world.
This author is the opposite of that, and why wouldn’t he be! Nolan Gasser is a composer himself, and the chief architect of Pandora’s Music Genome Project. Ever wonder how streaming services have become so good at predicting what a good next song to play is, or what your tastes are in general? It all started with this. I won’t go into the history here (it’s in this book), except to say that a massive amount of resources & human power went into research & development, resulting in the granddaddy of all predictive music algorithms. And while I find all of them to be lesser than an actual human DJ making choices, as the years pass they’re much more hit than miss.
Now for the book. Wow. It delivers on the title’s promise in spades. About 2/5ths of it is on music theory – and while I learned most of it in college, it was an incredible refresher. Even though the author says you can skip all that and get to the actual “why you like it” part, I think you’ll understand his reasoning much better if you absorb as much theory as possible. He also includes “interlude” chapters that connect to science, math, culture & psychology. They’re short but quite illuminating.
The rest of the book is broken into sections focusing on musical “genotypes”. They’re umbrella terms for a fan’s primary taste: musical theater, pop, rock, jazz, hip hop, electronica, world & classical. Nolan says some stuff about the deficiencies of genre labeling that made me love this book from the get-go, so he’s well aware of how reductive these categories are. Even with that caveat, he manages to flesh out each genotype & connect these imaginary fans’ tastes to broader spectra of music. It’s fun trying to figure out what genotype you are. For me – as you can predict – I didn’t align perfectly with any of them. The book promised a test at its website, but sadly that page is still blank. As someone who loves tests/surveys/questionnaires, I hope he eventually gets to it.
In the end, this book is kinda like a story or work of non-fiction that claims to have the answer to “the meaning of life”. It never quite reveals the magic you were hoping for, but it’s so well done that where it compels your brain to go is worth the trip. If anything, it gives you the tools to find the answers yourself.
There’s a good chance Nolan Gasser & I don’t share primary musical tastes, but it’s clear we are kindred spirits. He fully understands how vital it is to be open to music of all kinds & connections wherever you can find them. And because of that, I dedicate this week’s song to him:
REC – “Polymath” (from the album Syzygy for the Weird)
Is this the kind of book you’d enjoy reading? Do you think you fall into one of those genotypes, or do you straddle the lines too? Are you a fan of predictive algorithms of any kind, or do you find them inadequate, creepy & controlling? Discuss dammit!