Piano Genie

Autor: Chris Donahue, Ian Simon, Sander Dieleman
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
Sound (cs.SD)
Computer science
Speech recognition
Interface (computing)
Computer Science - Human-Computer Interaction
Machine Learning (stat.ML)
Data_CODINGANDINFORMATIONTHEORY
02 engineering and technology
Computer Science - Sound
Machine Learning (cs.LG)
Human-Computer Interaction (cs.HC)
Generative modeling
Statistics - Machine Learning
Audio and Speech Processing (eess.AS)
Intelligence amplification
Control theory
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
0501 psychology and cognitive sciences
050107 human factors
Sequence
05 social sciences
Piano
020206 networking & telecommunications
Recurrent neural network
Encoder
Electrical Engineering and Systems Science - Audio and Speech Processing
Zdroj: Proceedings of the 24th International Conference on Intelligent User Interfaces.
DOI: 10.1145/3301275.3302288
Popis: We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user's input for the encoder output, and play the decoder's prediction each time the user presses a button. To improve the intuitiveness of Piano Genie's performance behavior, we impose musically meaningful constraints over the encoder's outputs.
Comment: Published as a conference paper at ACM IUI 2019
Databáze: OpenAIRE
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