UC San Diego News Center
During COSMOS 2019 at UC San Diego, Music prof. Shlomo Dubnov (right) discusses machine learning architecture for tool to convert musical notation between musical styles with COSMOS students, including co-author Conan Lu, a high school senior (far left).
Can artificial intelligence enable computers to translate a musical composition between musical styles – e.g., from pop to classical or to jazz? According to a professor of music at UC San Diego and a high school student, they have developed a machine learning tool that does just that.
“People are more familiar with machine learning that can automatically convert an image in one style to another, like when you use filters on Instagram to change an image’s style,” said UC San Diego computer music professor Shlomo Dubnov. “Past attempts to convert compositions from one musical style to another came up short because they failed to distinguish between style and content.”
To fix that problem, Dubnov and co-author Conan Lu developed ChordGAN – a conditional generative adversarial network (GAN) architecture that uses chroma sampling, which only records a 12-tones note distribution note distribution profile to separate style (musical texture) from content (i.e., tonal or chord changes).
