Peer-recommendation services like Last.fm and Pandora are pretty good at leveraging the power of the community to help you discover new music, but a recent grant from the National Science Foundation to the College of Charleston aims to take the concept to the next level, by creating a search engine that "listens" to music and creates critical comparisons between works. The system, as described by Ars Technica, involves a neural network that is trained to recognized the composer and style of music, an evaluation engine that's supposed to simulate human taste, and a set of objective metrics like pitch, tempo, and duration. The results are then combined and the system can then recommend matches to find similar music. The researchers have already demoed a similar system with good results, so here's hoping the grant money helps them refine things further -- we've been looking way too long for the next Wham!
[Image from O'Reilly's Digital Media Blog]