# Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions

arXiv: https://arxiv.org/abs/2509.14785 · HTML: https://mlsanity.com/paper/2509.14785/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2025.

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

- [Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings](https://mlsanity.com/paper/2607.03806/) (2026)
- [SceneBind: Binding What and Where Across Vision, Audio and Language](https://mlsanity.com/paper/2607.15265/) (2026)

## Abstract (arXiv)

Contrastive language--audio pretraining (CLAP) has achieved remarkable success as an audio--text embedding framework, but existing approaches are limited to monaural or single-source conditions and cannot fully capture spatial information. The central challenge in modeling spatial information lies in multi-source conditions, where the correct correspondence between each sound source and its location is required. To tackle this problem, we propose Spatial-CLAP, which introduces a content-aware spatial encoder that enables spatial representations coupled with audio content. We further propose spatial contrastive learning (SCL), a training strategy that explicitly enforces the learning of the correct correspondence and promotes more reliable embeddings under multi-source conditions. Experimental evaluations, including downstream tasks, demonstrate that Spatial-CLAP learns effective embeddings even under multi-source conditions, and confirm the effectiveness of SCL. Moreover, evaluation on unseen three-source mixtures highlights the fundamental distinction between conventional single-source training and our proposed multi-source training paradigm. These findings establish a new paradigm for spatially-aware audio--text embeddings.