Friday 1:30-3:30pm / LONG 217
This project explores stochastic techniques to computationally identify and emphasize aesthetic aspects of music. Currently, we are studying ways to apply Zipf Law to computer music.
We have extended earlier results (Voss and Clarke, 1975; Zipf, 1949) by identifying a set of measurable attributes of music that may exhibit Zipf-Mandelbrot distributions. These measurable attributes (metrics) include pitch of notes, duration of notes, harmonic and melodic intervals, and many others. Experiments on corpora from various music genres (e.g., baroque, classical, 12-tone, jazz, rock, punk rock) demonstrate the validity of the approach.
Currently, we are investigating ways to combine our metrics with AI techniques, such neural networks and genetic algorithms, to analyze and help generate music that sounds "pleasing, beautiful, harmonious." Related application areas include music education, music therapy, music recognition by computers, and computer-aided music analysis/composition.
Also, see some earlier results from this project.
Email Dr. Manaris to set up an appointement. Include a few words about your background and your student ID.