GrooveNet: Real-time Music-Driven Dance Movement Generation using Artificial Neural Networks
Omid Alemi and Jules Françoise and Philippe Pasquier
Complementary Materials for the Submission to the Workshop on Machine Learning for Creativity
Learning and Generating Movement Patterns
Dancing with Training Songs
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FCRBM - Cooked Features
Hidden Units: 500 | Factors: 500 | Order: 30 | Frame Rate: 60
Audio Features: 84-Dimensions:
low-level features (RMS level, Bark bands)
spectral features (energy in low/middle/high frequencies, spectral centroid, spectral spread, spectral skewness, spectral kurtosis, spectral rolloff, spectral crest, spectral flux, spectral complexity),
timbral Features (Mel-Frequency Cepstral Coefficients, Tristimulus),
melodic Features (pitch, pitch salience and confidence, inharmonicity, dissonance).
Based on audio track 1:
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Output 3
Based on audio track 2:
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Output 6
Based on audio track 3:
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Output 9
Dancing with Unheard Songs
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FCRBM - Cooked Features
Hidden Units: 500 | Factors: 500 | Order: 30 | Frame Rate: 60
Audio Features: 84-Dimensions:
low-level features (RMS level, Bark bands)
spectral features (energy in low/middle/high frequencies, spectral centroid, spectral spread, spectral skewness, spectral kurtosis, spectral rolloff, spectral crest, spectral flux, spectral complexity),
timbral Features (Mel-Frequency Cepstral Coefficients, Tristimulus),
melodic Features (pitch, pitch salience and confidence, inharmonicity, dissonance).
Output 1 •
Output 2 •
Output 3 •
Output 4 •
Output 5 •
Output 6
Fun Material!
Fun 1 •
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Fun 5