The cocktail party effect is the ability to focus on a specific human voice while filtering out other voices or background noise. The ease with which humans perform this trick belies the challenge that scientists and engineers have faced in reproducing it synthetically. By and large, humans easily outperform the best automated methods for singling out voices.
A 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. By comparison, machines are poor at this task.
Today, that looks to be changing thanks to the work of Andrew Simpson and pals at the University of Surrey in the U.K. 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.
Their approach showcases the huge advances that have been made in recent years in machine learning and neural networks. 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.
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