It began with an argument. Tristan Jehan and Brian Whitman met as Ph.D. candidates at MIT’s Media Lab. Both were amateur musicians passionate about the ways technology might recommend songs based on a listener’s tastes. Both were convinced that “collaborative filtering,” a trendy means of achieving that goal, was woefully inadequate. Their disagreement? Jehan’s research focused on teaching computers to capture the sonic elements of music, while Whitman’s studied the cultural and social components. In combining the two approaches they created the Echo Nest, one of the most important digital music companies few have heard about.
The rest: The big music brain that knows what you like - Fortune Tech
In this article, we’ll look at the statistics gathered from 1300 choruses, verses, etc. of popular songs to discover the answer to a few basic questions. First we’ll look at the relative popularity of different chords based on the frequency that they appear in the chord progressions of popular music. Then we’ll begin to look at the relationship that different chords have with one another. For example, if a chord is found in a song, what can we say about the probability for what the next chord will be that comes after it?
via I analyzed the chords of 1300 popular songs for patterns. This is what I found. | Blog – Hooktheory
"Amassing more songs and managing the metadata and organizing the music library all begin to cannibalize the pleasure of the music itself. Or rather, these data-driven pleasures mediate our experience of music in a different way from what we knew before mp3s. The music becomes more like information, requiring less of a sensual surrender. Girl Talk seems emblematic of music created to suit this new aesthetic; classifying the samples becomes inseparable from the pleasures of listening to it."