Music Mapping

t-SNE + Music

Stephen Phillips
@mawsonguy

This is a map of the 5000 

most popular acts in music today. 


It was created using word2vec, t-sne,

d3js and 100k Spotify playlists. 


The biggest names appear on the most playlists. The closer two names are together, the more likely they appear in similar playlists.

ALL GENRES

POP

HIP HOP

INDIE 

ROCK

ELECTRONIC


If you have a big fast computer, try it out

http://popgun.me/tsne

Algorithms

Word2Vec is a Neural Network Language model developed by Tomas Mikolov and other engineers at Google. It was released as an open source project in 2013.The model is capable of organizing concepts based on their distributional similarity without receiving prior instructions about the manner in which these concepts are related. It creates meaningful representations of words that captures both syntactic and semantic information of a word.

t-distributed stochastic neighbor embedding (t-SNE) is a machine learning algorithm for dimensionality reduction developed by Laurens van der Maaten and Geoffrey Hinton. It is a nonlinear dimensionality reduction technique that is particularly well suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points. [2]

About Me

From 2009 I led a music startup called We Are Hunted. It was one of the first music charts powered by social media and was a popular tool for music discovery. It was later acquired by Twitter in 2012. I worked on Twitter Music in 2013/2014. Since leaving Twitter I've been thinking a lot about new models for music discovery. This story is part of a series I am writing exploring the past and future of music app design.