# A Bayesian Mallows approach to non-transitive pair comparison data: how human are sounds?

## TL;DR

This paper introduces a Bayesian Mallows model to analyze non-transitive pairwise comparison data, revealing insights into human perception of sounds as human or non-human, with applications in sound design and audio industry.

## Contribution

It develops a Bayesian Mallows approach with a mixture extension to handle non-transitive preferences and heterogeneity in listener data.

## Key findings

- Model effectively captures preference inconsistencies.
- Identifies factors influencing perception of human sounds.
- Provides a framework for designing more human-like computer sounds.

## 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.