Washington, June 14 (ANI): Electrical engineers at UC San Diego have designed novel music discovery games on Facebook in order to improve their experimental music search engine that is capable of listening to new songs and accurately labelling them with words-with no help from humans.
The engineers launched the music discovery games on Facebook as an application called Herd It.
"The Facebook games are a lot of fun and a great way to discover new music," said Gert Lanckriet, the electrical engineering professor and machine learning expert from the UC San Diego Jacobs School of Engineering steering the project.
"At the same time, the games deliver the data we need to teach our computer audition system to listen to and describe music like humans do.
"To play Herd It, log in to Facebook, open the Herd It app, select a genre of music, and start listening to song clips and playing the games.
"Some games ask users to identify instruments, while others focus on music genres, artist names, emotions triggered by the song, and activities you might do while listening to a song.
"The more your answers align with the rest of the online crowd playing the game at the same time, the more points you score," he added.
"The more examples of romantic songs our search engine is exposed to, the more accurately it will be able to identify romantic songs it has never heard before," said Luke Barrington, the UC San Diego electrical engineering Ph.D. student leading the project.
"Once enough people play our new music discovery games on Facebook, I'll have the data I need to both improve our search engine and finish my Ph.D.," he added.
For the music search engine from UC San Diego to "listen and describe music like a human," it must find patterns in the songs using the tools of machine learning.
For example, for the system to learn to identify and label romantic songs, it must be exposed to many different romantic songs during the training period. The Facebook games provide the data necessary for the algorithms to learn to label songs on their own.
Once trained, the system can identify romantic songs that it has never before encountered.
The research was presented at IEEE International Conference on Acoustics, Speech, and Signal Processing. (ANI)
The engineers launched the music discovery games on Facebook as an application called Herd It.
"The Facebook games are a lot of fun and a great way to discover new music," said Gert Lanckriet, the electrical engineering professor and machine learning expert from the UC San Diego Jacobs School of Engineering steering the project.
"At the same time, the games deliver the data we need to teach our computer audition system to listen to and describe music like humans do.
"To play Herd It, log in to Facebook, open the Herd It app, select a genre of music, and start listening to song clips and playing the games.
"Some games ask users to identify instruments, while others focus on music genres, artist names, emotions triggered by the song, and activities you might do while listening to a song.
"The more your answers align with the rest of the online crowd playing the game at the same time, the more points you score," he added.
"The more examples of romantic songs our search engine is exposed to, the more accurately it will be able to identify romantic songs it has never heard before," said Luke Barrington, the UC San Diego electrical engineering Ph.D. student leading the project.
"Once enough people play our new music discovery games on Facebook, I'll have the data I need to both improve our search engine and finish my Ph.D.," he added.
For the music search engine from UC San Diego to "listen and describe music like a human," it must find patterns in the songs using the tools of machine learning.
For example, for the system to learn to identify and label romantic songs, it must be exposed to many different romantic songs during the training period. The Facebook games provide the data necessary for the algorithms to learn to label songs on their own.
Once trained, the system can identify romantic songs that it has never before encountered.
The research was presented at IEEE International Conference on Acoustics, Speech, and Signal Processing. (ANI)
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