AI News, Machine-Learning Algorithm Watches Dance Dance Revolution, Then Creates Dances of Its Own

Machine-Learning Algorithm Watches Dance Dance Revolution, Then Creates Dances of Its Own

Their system—called Dance Dance Convolution—takes as an input the raw audio files of pop songs and produces dance routines as an output.

Beginner-level games have steps on quarter and eighth notes, but higher difficulty dances have 16thnote steps and some patterns involving 12thand 24thnotes.

Music research has been hampered in the past because copyright issues can prevent songs being used in research (or at least being passed on along with the results).

Donahue and co say that one online repository, called Stepmania Online, stores over 350 gigabytes of dance charts on more than 100,000 songs.

For this research, the team focuses on two smaller data sets consisting of recordings plus dance charts.

The first contains 90 songs choreographed by a single author, who has produced charts of five levels of difficulty for each song.

The team then increases the data set by creating a mirror image of each chart—for example, by swapping left for right and up for down (or both).

The result is a data set of 35 hours of music in the form of raw audio files with more than 350,000 steps.

Donahue and co then use 80 percent of the music to train the machine-learning algorithm to recognize times for step placement.

“Our experiments establish the feasibility of using machine learning to automatically generate high-quality DDR charts from raw audio,” say Donahue and co.

“By combining insights from musical onset detection and statistical language modeling, we have designed and evaluated a number of deep-learning methods for learning to choreograph,” they say.

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