AI News, Deep Learning Machine Solves the Cocktail Party Problem

Deep Learning Machine Solves the Cocktail Party Problem

The cocktail party effect is the ability to focus on a specific human voice while filtering out other voices or background noise.

particularly challenging cocktail party problem is in the field of music, where humans can easily concentrate on a singing voice superimposed on a musical background that includes a wide range of instruments.

These guys have used some of the most recent advances associated with deep neural networks to separate human voices from the background in a wide range of songs.

And it paves the way for a more general solution to the famous cocktail party problem which should allow, among other things, the vocals to be easily separated from the music they accompany.

They start with a database of 63 songs that are available as a set of individual tracks that each contain a different instrument or voice, as well as the fully mixed version of the song.

So the network begins with these parameters set randomly and then gradually improves the settings each time it scans through the database, which it did over a hundred iterations.

“These results demonstrate that a convolutional deep neural network approach is capable of generalizing voice separation, learned in a musical context, to new musical contexts,” say the team.

Simpson and co of even compared their results to those from a conventional cocktail party algorithm applied to the same data. “The main advantage of the deep neural network appears to be in its general learning of what ‘vocal’ sounds are,” they say.

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