  We are interested in learning how listeners perceive sounds as having human
origins. An experiment was performed with a series of electronically
synthesized sounds, and listeners were asked to compare them in pairs. We
propose a Bayesian probabilistic method to learn individual preferences from
non-transitive pairwise comparison data, as happens when one (or more)
individual preferences in the data contradicts what is implied by the others.
We build a Bayesian Mallows model in order to handle non-transitive data, with
a latent layer of uncertainty which captures the generation of preference
misreporting. We then develop a mixture extension of the Mallows model, able to
learn individual preferences in a heterogeneous population. The results of our
analysis of the musicology experiment are of interest to electroacoustic
composers and sound designers, and to the audio industry in general, whose aim
is to understand how computer generated sounds can be produced in order to
sound more human.
