Poster Title
P-01 Art to Influence Creativity in Symbolic Music Completion
Department
Computing
Abstract
Advances in Recurrent Neural Network (RNN) techniques have caused an explosion of problems posed that revolve around the mass analysis and generation of sequential data, including symbolic music. Building off the work of Nathaniel Patterson’s Musical Autocomplete: An LSTM Approach, we extend this problem of continuing a composition by examining the creative impact that injecting latent-space encoded image data, specifically fine art from the WikiArt Dataset (Saleh & Elgammal), has on the musical output of RNN architectures designed for autocomplete. For comparison purposes with Patterson, we will also be using a corpus of Erik Satie’s piano music for training, validation, and testing.
Location
Buller Hall, Student Lounge
Start Date
3-11-2022 1:30 PM
End Date
3-11-2022 3:30 PM
P-01 Art to Influence Creativity in Symbolic Music Completion
Buller Hall, Student Lounge
Advances in Recurrent Neural Network (RNN) techniques have caused an explosion of problems posed that revolve around the mass analysis and generation of sequential data, including symbolic music. Building off the work of Nathaniel Patterson’s Musical Autocomplete: An LSTM Approach, we extend this problem of continuing a composition by examining the creative impact that injecting latent-space encoded image data, specifically fine art from the WikiArt Dataset (Saleh & Elgammal), has on the musical output of RNN architectures designed for autocomplete. For comparison purposes with Patterson, we will also be using a corpus of Erik Satie’s piano music for training, validation, and testing.
Acknowledgments
Advisor: Rodney Summerscales