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The popular Internet radio site Pandora performs a similar service, breaking down songs and analyzing their attributes. Founded in 2000, the site allows users to choose a song or artist, and then finds similar songs. Users can quickly fine-tune the results to create a highly personalized streaming radio station.

But Pandora’s technology is “100 percent manual,” according to Tim Westergren, the company’s chief strategy officer and founder. Through the Music Genome Project, a team of musicians evaluates songs, scoring them according to 400 different attributes. Once these attributes are identified, Westergren says, “it’s pretty straightforward math” to make recommendations to users. He says that Pandora is open to incorporating more automated approaches to analyzing songs but adds, “We haven’t yet found one that we think is really value added to what we’re doing.”

Other companies are also working on automatic analysis of music. The Echo Nest, a startup based in Somerville, MA, transforms the waveform patterns of songs according to simulations of how the human ear hears music. From there, the Echo Nest’s system applies filters that identify features of the song, such as tempo and pitch, according to company cofounder and CTO Tristan Jehan.

Once that’s done, the Echo Nest’s system combines this information with tagging information gleaned from blogs and other data posted on the Internet. It then applies machine-learning algorithms to identify features of songs that are commonly associated with specific tags, much as the UCSD researchers’ software does.

The difference, according to Jehan, is that instead of identifying complex patterns in the waveforms, the Echo Nest’s software focuses on features that would be recognized by a human listener.

Forrester Research analyst Sonal Gandhi, who follows the music industry, says that more automated methods of music search and recommendation could become important as on-demand music becomes more popular, and sites feel increased pressure to help users find new music.

Tim Crawford, a senior lecturer in computational musicology at Goldsmiths University of London, says that while analyzing music using computers is “a very interesting and promising area of research,” it will be hard to create a music search engine that’s both general and fully automatic. “Music similarity is such a personal and variable thing,” Crawford says. “Two heavy-metal tracks may seem highly similar to a classical-music expert like me, but entirely different to a heavy-metal enthusiast, who may in turn regard the music of Brahms and Tchaikovsky as very similar, which would be laughable to me.”

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Credit: Facebook/UCSD

Tagged: Computing, Web, machine learning, tagging, recommendation engine, music industry, music technology, music search engine

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