# Algorithms for Collaborative Harmonization

## TL;DR

This paper explores algorithms for aggregating musical harmonization suggestions, emphasizing effective collective representation and musical coherence, and finds Kemeny and plurality-based methods most effective.

## Contribution

It introduces and analyzes new algorithms for aggregating musical harmonizations, focusing on their effectiveness and computational complexity.

## Key findings

- Kemeny and plurality algorithms perform best in representation and coherence
- Proposed algorithms effectively aggregate harmonization suggestions
- Analysis includes complexity assessment of different methods

## Abstract

We consider a specific scenario of text aggregation, in the realm of musical harmonization. Musical harmonization shares similarities with text aggregation, however the language of harmony is more structured than general text. Concretely, given a set of harmonization suggestions for a given musical melody, our interest lies in devising aggregation algorithms that yield an harmonization sequence that satisfies the following two key criteria: (1) an effective representation of the collective suggestions; and (2) an harmonization that is musically coherent. We present different algorithms for the aggregation of harmonies given by a group of agents and analyze their complexities. The results indicate that the Kemeny and plurality-based algorithms are most effective in assessing representation and maintaining musical coherence.