# SALM: Spatial Audio Language Model with Structured Embeddings for Understanding and Editing

arXiv: https://arxiv.org/abs/2507.16724 · HTML: https://mlsanity.com/paper/2507.16724/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2025.

## Built on by (most cited first, 7 of 7)

- [SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models](https://mlsanity.com/paper/2511.06606/) (2025)
- [Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding](https://mlsanity.com/paper/2606.10738/) (2026)
- [PhaseCoder: Microphone Geometry-Agnostic Spatial Audio Understanding for Multimodal LLMs](https://mlsanity.com/paper/2601.21124/) (2026)
- [Sci-Phi: A Large Language Model Spatial Audio Descriptor](https://mlsanity.com/paper/2510.05542/) (2025)
- [AudioMotionBench: Evaluating Auditory Motion Perception in Audio LLMs](https://mlsanity.com/paper/2511.13273/) (2025)
- [OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents](https://mlsanity.com/paper/2609.23407/) (2026)
- [SonicWorld: Source-Centric Latent Dynamics for Spatial Audio Reasoning](https://mlsanity.com/paper/2610.17131/) (2026)

## Abstract (arXiv)

Spatial audio understanding is essential for accurately perceiving and interpreting acoustic environments. However, existing audio-language models exhibit limitations in processing spatial audio and perceiving spatial acoustic scenes. To address this gap, we propose the Spatial Audio Language Model (SALM), a novel framework that bridges spatial audio and language through multi-modal contrastive learning. SALM integrates a text encoder with a dual-branch audio encoder that decomposes spatial sound into semantic and spatial components via structured audio embeddings. Key features of SALM include seamless alignment between spatial audio and natural language, both separate and joint extraction of spatial and semantic representations, zero-shot direction classification, and flexible support for spatial audio editing. Experimental results demonstrate that SALM effectively captures and aligns cross-modal representations, yielding well-structured audio embeddings. Furthermore, SALM enables advanced editing capabilities, such as modifying directional audio using text-based embeddings.