Psychological constraints on string-based methods for pattern discovery in polyphonic corpora

TL;DR

This paper investigates whether psychologically motivated weighting functions can enhance harmonic pattern discovery in polyphonic music corpora, focusing on the common-practice period chord progression and evaluating model performance with mean reciprocal rank.

Contribution

It introduces the application of psychologically motivated weighting functions to harmonic pattern discovery algorithms in polyphonic music analysis.

Findings

Weighting functions can influence the ranking of discovered harmonic patterns.

Certain weighting strategies improve the detection of conventional chord progressions.

The study provides a framework for integrating psychological insights into music pattern discovery.

Abstract

Researchers often divide symbolic music corpora into contiguous sequences of n events (called n-grams) for the purposes of pattern discovery, key finding, classification, and prediction. What is more, several studies have reported improved task performance when using psychologically motivated weighting functions, which adjust the count to privilege n-grams featuring more salient or memorable events (e.g., Krumhansl, 1990). However, these functions have yet to appear in harmonic pattern discovery algorithms, which attempt to discover the most recurrent chord progressions in complex polyphonic corpora. This study examines whether psychologically-motivated weighting functions can improve harmonic pattern discovery algorithms. Models using various n-gram selection methods, weighting functions, and ranking algorithms attempt to discover the most conventional closing harmonic progression in the common-practice period, ii6-"I64"-V7-I, with the progression's mean reciprocal rank serving as an evaluation metric for model comparison.