The Institute - March 2018 - 6

REINVENTING
HOW WE
DISCOVER MUSIC
Pandora uses machine learning
to recommend artists
and predict hits
B Y M O N I CA R O Z E N F E L D

everything listeners do, including
which Pandora stations they save and
listen to most. That way, when a new
artist releases an album the algorithm
determines that listeners might like,
Pandora will recommend the music to
them, Perline says.
HELPIN G ARTI S TS S U C C EED

The data the company collects is helpful not only to listeners but to artists
as well. Its Artist Marketing Platform
gives the musicians free insights on,
for example, how many people have
listened to their songs and for how
long. They can submit new music
through the platform, with the potential for it to be played on Pandora.
Pandora in 2015 bought Next Big
Sound, a company that provides analytics not only from its own service but
also from third-party sources including
social media platforms, radio stations,
and video sites such as YouTube. Artists
can access Next Big Sound to analyze
the data and strategize on ways to reach
their fans. Data showing geographic
areas with concentrations of fans could
be used to plan a tour, for example. In
fact, Perline says, such analytics have
prompted bands to perform in towns
they otherwise might have skipped.

F YO U ' R E A F O L K R O C K FA N

like me, you might be surprised
when a music-streaming service suggests songs for you in other genres,
such as country or reggae-and
you actually enjoy them. Apple Music,
Pandora, Spotify, and similar services
are taking subtle cues from your listening habits, not only to recommend
new artists but also to create personalized playlists for you with tracks you've
likely never heard before.
To understand the technology making that possible, The
Institute spoke with IEEE Member
Josh Perline, a software engineer at
Pandora, in Oakland, Calif. Perline is
responsible for content personalization, which involves using data analytics and machine learning to automate
the creation of suggested playlists.
As one of the first music-streaming
services on the market, Pandora has
some 17 years of data to work with.
The company relies on data from its
Music Genome Project, an undertaking that analyzes hundreds of songs'
musical attributes, including which
6

TH E IN STITUTE MAR CH 2018

instruments are featured and the
range of notes and vocal sounds.
"We have the same tracks as every
other provider, but what keeps people
coming back to us is the strength of
our recommendations to help them
discover new music," Perline says.
RO L L I N G I N T H E D EEP

Say you're listening to Pandora's Adele
station. It not only plays the songs of
the Grammy award winner; it also
plays artists with a similar sound, as
well as artists from decades ago who
influenced the singer-songwriter's
music, Perline notes.
What differentiates Pandora, he
says, is the human input that informs
its algorithm on how to recommend
music. The algorithm, based on
machine learning, makes decisions by
incorporating listeners' preferences as
well as information about the songs.
Staff members working on Pandora's
Music Genome Project have backgrounds in musicology-the scholarly
analysis of music. They assign attributes to the songs-some of which

can have as many as 450 unique traits.
That can include the emotions the
lyrics convey, such as anger, sadness,
or joy; each instrument in the recording and whether it's synthesized or
acoustic; to what degree the vocals are
nasal-sounding; and sound effects.
The metadata allows the algorithm
to get much more precise. It not only
classifies a song as rap, for example,
but that of its sub-genre such as
proto-industrial hip-hop, a fusion of
industrial music and spoken words
combined with hip-hop rhythms and
vocals. "It gets extremely nuanced,
down to even working from the liner
notes," Perline says, referring to information such as the artist's biography
and the lyrics.
The service can also tweak
recommendations based on listeners' feedback. When people hear a
song, they can tap a "thumbs up" or
"thumbs down" icon. If "thumbs down"
is tapped the playlist skips to the next
song, and Pandora likely won't play
that tune or similar ones again. In
fact, the algorithm pays attention to

Next Big Sound has used its data to
make accurate predictions as well. It
determined several artists who would
win Grammy awards, for instance.
Data about the number of music
downloads and social media shares
can help inform the predictions.
Next Big Sound also can predict the
next big hit or emerging artist before
the music goes mainstream. That happened with rapper Lil Yachty, months
before he first hit the Billboard Hot 100
chart. Such predictions are made possible in part when data shows how fast
and from how many sources music has
been shared.
If, for example, a YouTube music
video uploaded by an unknown artist
with only two dozen subscribers to
his channel gets 5,000 hits in an hour,
"we can see something is hot," Perline
says. In other words, the algorithm
can spot the activity a song is receiving
and whether it surpasses the traffic it's
expected to get.
Perline sees music-streaming
services as leveling the playing field for
independent and undiscovered artists
by giving them exposure even if they
don't have a record deal. "I think that's
the direction music is going in," he says,
with more self-made artists navigating
their way to mainstream culture.
"Millions of tracks are available. Our
job is to get the right ones to you." ◆
THEINSTITUTE.IEEE.ORG

ISTOCKPHOTO

AND THE W I N N ER I S ...

http://theinstitute.ieee.org

Table of Contents for the Digital Edition of The Institute - March 2018