# Accompaniment Prompt Adherence: A Measure for Evaluating Music Accompaniment Systems

arXiv: https://arxiv.org/abs/2503.06346 · HTML: https://mlsanity.com/paper/2503.06346/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck](https://mlsanity.com/paper/2509.14052/) (2025)
- [PHALAR: Phasors for Learned Musical Audio Representations](https://mlsanity.com/paper/2605.03929/) (2026)

## Abstract (arXiv)

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.