# Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation

arXiv: https://arxiv.org/abs/2607.13903 · HTML: https://mlsanity.com/paper/2607.13903/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2026.

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

- [Fidelity Preference, Not Demographic Preference: A Pixel-Level Attribute-Sensitivity Audit of Image Aesthetic/Preference Scorers](https://mlsanity.com/paper/2608.23593/) (2026)
- [What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings](https://mlsanity.com/paper/2610.13247/) (2026)

## Abstract (arXiv)

Music aesthetics scoring plays a critical role in applications such as dataset curation, generative model evaluation, and reward modeling for music generation. Recent approaches rely on deep neural networks trained on human-annotated ratings, but these models may exploit spurious correlations rather than capturing perceptually meaningful aesthetics. In this work, we identify a previously underexplored failure mode in music evaluation models: genre-induced shortcut learning. Through a systematic analysis of SongEval, we show that biases in training data lead to strong correlations between genre-related features and predicted scores, causing the model to use them as a proxy for aesthetics. This results in systematic overestimation of pop music and undervaluation of high-quality samples from other genres, leading to predictions that are inconsistent with human preferences. To address this issue, we propose a training objective that jointly reweights hard samples and regularizes group-level performance, encouraging the model to learn genre-invariant representations of musicality. Experimental results demonstrate that our method reduces genre-dependent bias and improves alignment with human preferences, as reflected by gains in both cross-genre and within-genre preference alignment.