A BAYESIAN MALLOWS APPROACH TO NONTRANSITIVE PAIR COMPARISON DATA : HOW HUMAN ARE SOUNDS?

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Crispino , M , Arjas , E , Vitelli , V , Barrett , N & Frigessi , A 2019 , ' A BAYESIAN MALLOWS APPROACH TO NONTRANSITIVE PAIR COMPARISON DATA : HOW HUMAN ARE SOUNDS? ' , Annals of Applied Statistics , vol. 13 , no. 1 , pp. 492-519 . https://doi.org/10.1214/18-AOAS1203

Title: A BAYESIAN MALLOWS APPROACH TO NONTRANSITIVE PAIR COMPARISON DATA : HOW HUMAN ARE SOUNDS?
Author: Crispino, Marta; Arjas, Elja; Vitelli, Valeria; Barrett, Natasha; Frigessi, Arnoldo
Contributor: University of Helsinki, Department of Mathematics and Statistics
Date: 2019-03
Language: eng
Number of pages: 28
Belongs to series: Annals of Applied Statistics
ISSN: 1932-6157
URI: http://hdl.handle.net/10138/301335
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.
Subject: Nontransitive pairwise comparisons
ranking
Mallows model
Bayesian preference learning
recommender systems
musicology
acousmatic experiment
RANKING
MODEL
TIES
111 Mathematics
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