| cmmpc |
Description |
We are developing and testing models designed to explain, from first principles, our
perception and cognition of music. Projects we are currently undertaking include:• Algorithms
to Discover Musical Patterns—Our research involves putting specific aspects
of music perception, such as the discovery of certain types of musical patterns, on
an algorithmic footing. The resulting algorithms are evaluated by comparison with
human experts/listeners performing analogous tasks. We use disparities between algorithm
output and human response to shed light on the more nuanced strategies behind human
perception of music, thence to improve our models (Tom Collins, Robin Laney).• Computational
Modelling of Tonality Perception—A psychoacoustic model designed to explain
the feelings of expectation and resolution that are induced by various chord progressions
and scales. The model is currently being developed and experimentally tested by comparing
the model’s predictions with those given by humans (Andrew Milne, Robin Laney,
David Sharp).• Melodic emotions: Insight and Prediction—Examining the
role of melody in conveying emotions and whether there are characteristic melodic
features that can be used to predict a melody’s emotional impact on listeners
(Pauline Mouawad, Robin Laney, Chris Dobbyn).• Testing Computational Models
of Rhythm Perception using Polyrhythms—Rhythm perception models (including Ed
Large’s non-linear dynamics model) are tested with rhythms containing two different
pulses—such as three against four rhythms found in African-derived music (Vassilis
Angelis, Simon Holland, Martin Clayton). |
