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March 2019 A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds?
Marta Crispino, Elja Arjas, Valeria Vitelli, Natasha Barrett, Arnoldo Frigessi
Ann. Appl. Stat. 13(1): 492-519 (March 2019). DOI: 10.1214/18-AOAS1203

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 nontransitive 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 nontransitive 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.

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Marta Crispino. Elja Arjas. Valeria Vitelli. Natasha Barrett. Arnoldo Frigessi. "A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds?." Ann. Appl. Stat. 13 (1) 492 - 519, March 2019. https://doi.org/10.1214/18-AOAS1203

Information

Received: 1 December 2017; Revised: 1 June 2018; Published: March 2019
First available in Project Euclid: 10 April 2019

zbMATH: 07057437
MathSciNet: MR3937438
Digital Object Identifier: 10.1214/18-AOAS1203

Keywords: acousmatic experiment , Bayesian preference learning , Mallows model , musicology , Nontransitive pairwise comparisons , ranking , recommender systems

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 1 • March 2019
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