Most people find new music on the airwaves, in magazines or from friends. Software may provide another avenue, by analyzing the music you like and recommending tunes, even from groups you’ve never heard of.
At least that’s what some researchers and vendors believe, and they’ve been developing technologies to make it happen. Among them, Gracenote Corp. said at the Consumer Electronics Show in Las Vegas this month that it will offer a product for online music stores by midyear that will let them make smarter music recommendations for their customers. And a project partially funded by the European Union said this month it is ready to start licensing a handful of similar technologies to service providers and consumer electronics makers.
The basic goal is to go beyond the names of artists and genres to help people find music they like, and instead to analyze the properties of a person’s music, such as its rhythm, tempo and energy level, to unearth similar tunes.
Early efforts relied mainly on signal processing techniques to uncover low-level similarities in music, such as its tempo and mood, according to Xavier Serra, who is managing the E.U.-funded SIMAC project at Barcelona’s Pompeu Fabra University. That was enough to roughly group tunes with similar properties, but might still have linked a fast-paced classical overture with a thumping techno beat.
More recent efforts incorporate other data as well, such as input from music fans and reviewers, which is appended to songs stored in giant databases that contain millions of tunes.
Gracenote said it also has its own team of experts who tag songs with one of 1,600 “micro genres” used to link similar music styles from a variety of roots. For example, it said, “Classic Motown and Psychedelic Pop are fairly different musically and are traditionally presented under separate categories of R&B and Rock, but are still strongly related and complementary from other perspectives.”
The company already offers products for identifying the tracks on a home-made music CD and for organizing related tunes into a playlist. When its Gracenote Discover product comes out later this year it hopes it will be used by online stores, MP3 makers and others to help end users find new music.
SIMAC also uses information from fans and reviews alongside signal processing to uncover related music. One of three products it hopes to license to the music and consumer electronics industries goes a step further. It draws on FOAF, or Friend of Friend, which is developing a way to make home pages on the Web readable by computers, so people can track down others with related interests.
Merging information extracted from a person’s music collection with other factors in their FOAF profile, such as their age and socioeconomic background, as well as their explicit music preferences, will allow the system to filter results and produce better matches, Xavier said.
Such products come with a risk for service providers. Offering too many questionable matches can undermine customers’ confidence in an online store, Gracenote admits. And the systems can require some intervention from end users just to get the style of music right. “If you’re a hip-hop fan, you might get 80 per cent hip hop, but you might also get 20 per cent techno, so you give the user the possibility to filter the selections,” Xavier said.