# Human-CLAP: Human-perception-based contrastive language-audio pretraining

arXiv: https://arxiv.org/abs/2506.23553 · HTML: https://mlsanity.com/paper/2506.23553/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [MuQ-Eval: An Open-Source Per-Sample Quality Metric for AI Music Generation Evaluation](https://mlsanity.com/paper/2603.22677/) (2026)
- [Aligning Audio Captions with Human Preferences](https://mlsanity.com/paper/2509.14659/) (2025)
- [The Sound of Absence: Audio-Language Embedding Models Struggle with Negation](https://mlsanity.com/paper/2607.12290/) (2026)
- [ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models](https://mlsanity.com/paper/2601.14157/) (2026)

## Abstract (arXiv)

Contrastive language-audio pretraining (CLAP) is widely used for audio generation and recognition tasks. For example, CLAPScore, which utilizes the similarity of CLAP embeddings, has been a major metric for the evaluation of the relevance between audio and text in text-to-audio. However, the relationship between CLAPScore and human subjective evaluation scores is still unclarified. We show that CLAPScore has a low correlation with human subjective evaluation scores. Additionally, we propose a human-perception-based CLAP called Human-CLAP by training a contrastive language-audio model using the subjective evaluation score. In our experiments, the results indicate that our Human-CLAP improved the Spearman's rank correlation coefficient (SRCC) between the CLAPScore and the subjective evaluation scores by more than 0.25 compared with the conventional CLAP.