Accompaniment Prompt Adherence: A Measure for Evaluating Music Accompaniment Systems
Abstract
Generative systems of musical accompaniments are rapidly growing, yet there are no standardized metrics to evaluate how well generations align with the conditional audio prompt. We introduce a distribution-based measure called "Accompaniment Prompt Adherence" (APA), and validate it through objective experiments on synthetic data perturbations, and human listening tests. Results show that APA aligns well with human judgments of adherence and is discriminative to transformations that degrade adherence. We release a Python implementation of the metric using the widely adopted pre-trained CLAP embedding model, offering a valuable tool for evaluating and comparing accompaniment generation systems.
Cite
@article{arxiv.2503.06346,
title = {Accompaniment Prompt Adherence: A Measure for Evaluating Music Accompaniment Systems},
author = {Maarten Grachten and Javier Nistal},
journal= {arXiv preprint arXiv:2503.06346},
year = {2025}
}
Comments
Accepted for publication at the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025)