# Automatic Melody Harmonization with Triad Chords: A Comparative Study

arXiv: https://arxiv.org/abs/2001.02360 · HTML: https://mlsanity.com/paper/2001.02360/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [SingSong: Generating musical accompaniments from singing](https://mlsanity.com/paper/2301.12662/) (2023)
- [Anticipatory Music Transformer](https://mlsanity.com/paper/2306.08620/) (2023)
- [Melody transcription via generative pre-training](https://mlsanity.com/paper/2212.01884/) (2022)
- [SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias](https://mlsanity.com/paper/2209.06054/) (2022)
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- [SongCreator: Lyrics-based Universal Song Generation](https://mlsanity.com/paper/2409.06029/) (2024)
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- [Emotion-Driven Melody Harmonization via Melodic Variation and Functional Representation](https://mlsanity.com/paper/2407.20176/) (2024)
- [Hookpad Aria: A Copilot for Songwriters](https://mlsanity.com/paper/2502.08122/) (2025)
- [Choir Transformer: Generating Polyphonic Music with Relative Attention on Transformer](https://mlsanity.com/paper/2308.02531/) (2023)
- [Emotion-Conditioned Melody Harmonization with Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2306.03718/) (2023)
- [Incorporating Structure and Chord Constraints in Symbolic Transformer-based Melodic Harmonization](https://mlsanity.com/paper/2512.07627/) (2025)
- [Evaluating Co-Creativity using Total Information Flow](https://mlsanity.com/paper/2402.06810/) (2024)
- [Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience](https://mlsanity.com/paper/2110.12855/) (2021)
- [Pay (Cross) Attention to the Melody: Curriculum Masking for Single-Encoder Melodic Harmonization](https://mlsanity.com/paper/2601.16150/) (2026)

## Abstract (arXiv)

Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating and comparing the performance of a set of canonical approaches to this task, including a template matching based model, a hidden Markov based model, a genetic algorithm based model, and two deep learning based models. The evaluation is conducted on a dataset of 9,226 melody/chord pairs we newly collect for this study, considering up to 48 triad chords, using a standardized training/test split. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants.