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 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]