Washington, Oct 28 : Electrical engineers at UC San Diego have created a new kind of music search engine that listens to music, and could even beat Genius - the music recommendation system in Apple''s iTunes.

It was found that Genius apparently captures acoustic similarities among songs within the same playlist.

They also discovered that the music recommender they built from scratch could generate song playlists that human subjects thought were as good as those that Genius generates.

The current study involved human evaluation of music recommendation systems and found that the new system works for songs that Genius knows nothing about.

"Our goal is to make a music recommendation tool that is as good as or better than Genius, but that does not require massive amounts of user data. The system we are developing can analyse and recommend completely unknown songs by new bands as accurately as it analyses the most popular hits," said UC San Diego electrical engineering Ph. D. student Luke Barrington.

In fact, he used the same underlying technology to create a series of music discovery games for Facebook and a new kind of music search engine that will be available for beta testing next week.

As Genius is a proprietary system whose secrets are not available to the public, the researchers studied it by testing its song recommendations against comparable song suggestions from experimental music recommender systems that they fully understood.

The researchers found that the playlist generator they built using their own algorithms performed as well as Genius under certain conditions.

As compared to Genius, which uses information about the songs people buy and listen to in iTunes in order to learn which songs in any iTunes library are related, the UCSD music recommender relies on auto-tagging algorithms that use machine learning to label songs with descriptive words based only on the acoustic content of the songs.

"Our computer system works by listening to the music - it doesn''t know anything about artists or albums or charts. In some trials of our survey, we tried to remove these biases from the human listeners by hiding the names of the songs and artists and making sure that subjects liked the seed songs but had not heard them before," said Barrington.

In these cases, the researchers found that people liked playlists generated by the UCSD auto-tagging algorithms as often as they liked Genius playlists.

This partial parity with Genius underscores how the UCSD auto-tagging algorithms can be used to generate high quality music playlists that incorporate lesser-known and unknown songs.

Genius currently ignores relatively unknown songs because it lacks adequate wisdom from iTunes customers about how these songs connect to other songs.

Systems like the auto-tagging music algorithms developed at UCSD could be useful in filling in the "blind spots" in Genius and other collaborative filtering systems that rely on the wisdom of the masses to generate playlists.

"We weren''t expecting our system to beat Genius at making playlists based on the most popular songs - our system doesn''t know about artists, popularity, release dates, albums or anything else that the average music fan is aware of. Once we add that information in, we think we can build something that is really smarter than Genius," said Barrington.

The study was presented at the 2009 International Society for Music Information Retrieval Conference in Kobe, Japan. (ANI)