Date of Submission
Project Advisor 1
With the widespread proliferation of AI technology, deep architectures — many of which are based on neural networks — have been incredibly successful in a variety of different research areas and applications. Within the relatively new domain of Music Information Retrieval (MIR), deep neural networks have also been successful for a variety of tasks, including tempo estimation, beat detection, genre classification, and more. Drawing inspiration from projects like George E. Lewis's Voyager and Al Biles's GenJam, two pioneering endeavors in human-computer interaction, this project attempts to tackle the problem of expressive music generation and seeks to create a Symbolic Music Transformer as a real-time musical improvisation companion, exploring the potential of AI to enhance the human experience of music. We successfully manage to implement the first iteration of a Transformer that can generate musical output. While the model struggles to generalize to a variety of inputs — likely due to limited training resources and data used while training — it can learn the structure of encoded midi-sequences and can generate expressive MIDI performances. We also present a working prototype of a performance environment built with Max/MSP which can parse auditory information in real-time and serve as the interface between the model and the musician.
Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College.
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Shirodkar, Arnav, "A Symbolic Music Transformer for Real-Time Expressive Performance and Improvisation" (2023). Senior Projects Fall 2023. 54.
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