Skip to Main content Skip to Navigation
Journal articles

A Bayesian Mallows Approach to Non-Transitive Pair Comparison Data: How Human are Sounds?

Marta Crispino 1 Elja Arjas 2, 3 Valeria Vitelli 3 Natasha Barrett 4 Arnoldo Frigessi 3, 5
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
Abstract : 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.
Document type :
Journal articles
Complete list of metadata

Cited literature [56 references]  Display  Hide  Download
Contributor : Marta Crispino <>
Submitted on : Tuesday, January 8, 2019 - 9:42:23 AM
Last modification on : Tuesday, May 11, 2021 - 11:37:39 AM
Long-term archiving on: : Tuesday, April 9, 2019 - 3:32:08 PM


Files produced by the author(s)




Marta Crispino, Elja Arjas, Valeria Vitelli, Natasha Barrett, Arnoldo Frigessi. A Bayesian Mallows Approach to Non-Transitive Pair Comparison Data: How Human are Sounds?. Annals of Applied Statistics, Institute of Mathematical Statistics, 2019, 13 (1), pp.492-519. ⟨10.1214/18-AOAS1203⟩. ⟨hal-01972952⟩



Record views


Files downloads