Event Title

P-01 Art to Influence Creativity in Symbolic Music Completion

Location

Buller Hall, Student Lounge

Start Date

3-11-2022 1:30 PM

End Date

3-11-2022 3:30 PM

Department

Computing

Description

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

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Mar 11th, 1:30 PM Mar 11th, 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.