New Music Search Engine Strays From the Herd
Posted by Lauren Rugani on November 16, 2009
Searching for new music can be a daunting task. Services like Apple’s iTunes or the website last.fm make it relatively easy to find music similar to songs or artists you already listen to. But type in “instrumentals for yoga class” and you probably won’t get very far.
Luke Barrington, a PhD student at the University of California San Diego, plans to change that with the beta version of his music search engine, Herd It, which he launched last week. His goal is to find and recommend music based on natural-language searches, providing users with both familiar and new songs that share acoustic qualities.
When he recently pitted his recommendation software against Apple’s music recommendation system, Genius, he found that users were equally satisfied with playlists suggested by both methods.
Genius is what Barrington labels a “metadata-based” system. The software associates music with information not necessarily related to the audio content, such as the name of the song, artist and album, and then averages statistics about how many users purchase and play each track. In other words, Genius is just telling you, “the people that listen to this song also listen to that song,” and suggests you do the same.
Barrington’s approach is more similar to the Pandora Radio model, or what he calls a content-based system, which builds playlists with songs that have similar sounds. Humans assign semantic descriptors to each of Pandora’s more than one million songs based on genre, emotion, instruments or vocals. For example, typing “Kings of Leon” into the search box produces a playlist featuring “electric rock instrumentation, a subtle use of vocal harmony, mild rhythmic syncopation, major key tonality and electric rhythm guitars.”
Herd It builds upon both Apple’s crowdsourcing mentality and Pandora’s natural-language song characterization, but incorporates machine learning to go beyond the capabilities of either system. Barrington created an algorithm to identify acoustic patterns in a song that predict a semantic tag. It then finds similar patterns in other songs and applies the tag automatically, eliminating the need to tag every song separately.
To this end, Barrington created a game on Facebook that allows users to ascribe qualities to thirty-second long clips of popular songs in a variety of genres. There are nearly 150 tags related to instrumentation, vocals, style, emotion, and even where you would prefer to listen to a song (relaxing at the beach, dancing at a party, while driving, etc.). When enough people independently agree on the same tag for a song, the algorithm learns to assign that tag to songs with similar acoustic patterns.
“Your definition of why a song is cool or why it goes well with another song may be quite different from mine,” Barrington said. “We’re hoping that the demographic information that we get from Facebook will help us to use Herd It data to learn demographic-specific representations of tags, like ‘teenage girls from San Diego think that this song rocks’ or ‘middleaged housewives from Europe would find this tune romantic’.” As the algorithm evolves, Herd It could one day provide personalized recommendations.
The machine gets smarter as more people play the game and as more music is available. Herd It’s current database is relatively small (about 10,000 songs) but Barrington hopes to partner with a major license holder for access to a more comprehensive collection. Since the algorithm is trained to read only acoustic qualities, it can be compatible with any music service. So while existing music recommendation engines build playlists around a specific song or artist, Herd It, in theory, should be able to create a playlist based on the query, “jazz trumpet melodies for a romantic dinner.”
There is even an option on Herd It for artists to upload their own music, which automatically receives relevant tags and is just as likely to appear on a playlist as any popular song with the same tags – an attribute not available through Genius, which requires that any new song they wish to add to their collection must first be listened to by a large number of users.
Barrington’s ultimate music recommendation engine would incorporate aspects of Genius’ that leverage huge amounts of user ratings and information that is hard to extract from just listening to the audio (e.g., popularity, release data, artist similarity, etc.), into Herd It so that any new song can immediately be added and recommended in the same context as older or more popular tunes. “Our system doesn’t know anything that the average music fan is aware of,” said Barrington. “Once we add that information in, we think we can build something that is really smarter than Genius.”
This entry was posted on November 16, 2009 at 2:39 PM and is filed under Computing, Smart Technology. Tagged: acoustics, algorithm, collaborative filtering, crowdsourcing, Herd It, machine learning, metadata, music search engine. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.