"Game-powered machine learning" opens door to Google for music

Can a computer be taught to automatically label every song on the Internet using sets of examples provided by unpaid music fans? University of California, San Diego engineers have found that the answer is yes, and the results are as accurate as using paid music experts to provide the examples, saving considerable time and money. In results published in the April 24 issue of the Proceedings of the National Academy of Sciences , the researchers report that their solution, called "game-powered machine learning," would enable music lovers to search every song on the web well beyond popular hits, with a simple text search using key words like "funky" or "spooky electronica." Searching for specific multimedia content, including music, is a challenge because of the need to use text to search images, video and audio. The researchers, led by Gert Lanckriet, a professor of electrical engineering at the UC San Diego Jacobs School of Engineering, hope to create a text-based multimedia search engine that will make it far easier to access the explosion of multimedia content online. That's because humans working round the clock labeling songs with descriptive text could never keep up with the volume of content being uploaded to the Internet. For example, YouTube users upload 60 hours of video content per minute, according to the company. In Lanckriet's solution, computers study the examples of music that have been provided by the music fans and labeled in categories such as "romantic," "jazz," "saxophone," or "happy." The computer then analyzes waveforms of recorded songs in these categories looking for acoustic patterns common to each.