# AudioMotionBench: Evaluating Auditory Motion Perception in Audio LLMs

arXiv: https://arxiv.org/abs/2511.13273 · HTML: https://mlsanity.com/paper/2511.13273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 26 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2410.19168/) (2024)
- [Kimi-Audio Technical Report](https://mlsanity.com/paper/2504.18425/) (2025)
- [AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension](https://mlsanity.com/paper/2402.07729/) (2024)
- [Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models](https://mlsanity.com/paper/2507.08128/) (2025)
- [GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities](https://mlsanity.com/paper/2406.11768/) (2024)
- [AudioBench: A Universal Benchmark for Audio Large Language Models](https://mlsanity.com/paper/2406.16020/) (2024)
- [Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models](https://mlsanity.com/paper/2503.02318/) (2025)
- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [BAT: Learning to Reason about Spatial Sounds with Large Language Models](https://mlsanity.com/paper/2402.01591/) (2024)
- [AERO: Audio Super Resolution in the Spectral Domain](https://mlsanity.com/paper/2211.12232/) (2022)
- [Learning Spatially-Aware Language and Audio Embeddings](https://mlsanity.com/paper/2409.11369/) (2024)
- [SAVGBench: Benchmarking Spatially Aligned Audio-Video Generation](https://mlsanity.com/paper/2412.13462/) (2024)
- [Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning](https://mlsanity.com/paper/2308.09514/) (2023)
- [SALM: Spatial Audio Language Model with Structured Embeddings for Understanding and Editing](https://mlsanity.com/paper/2507.16724/) (2025)
- [ASAudio: A Survey of Advanced Spatial Audio Research](https://mlsanity.com/paper/2508.10924/) (2025)
- [Learning spatial hearing via innate mechanisms](https://mlsanity.com/paper/2001.10605/) (2020)
- [Evaluation of Deep Audio Representations for Hearables](https://mlsanity.com/paper/2502.06664/) (2025)
- [Text2Move: Text-to-moving sound generation via trajectory prediction and temporal alignment](https://mlsanity.com/paper/2509.21919/) (2025)

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

- [Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding](https://mlsanity.com/paper/2606.10738/) (2026)
- [Spectro-Temporal Interference Confounds Phase Encoding in Spatial Audio Foundation Models](https://mlsanity.com/paper/2606.14820/) (2026)

## Abstract (arXiv)

Large Audio-Language Models (LALMs) have recently shown impressive progress in speech recognition, audio captioning, and auditory question answering. Yet, whether these models can perceive spatial dynamics, particularly the motion of sound sources, remains unclear. In this work, we uncover a systematic motion perception deficit in current ALLMs. To investigate this issue, we introduce AudioMotionBench, the first benchmark explicitly designed to evaluate auditory motion understanding. AudioMotionBench introduces a controlled question-answering benchmark designed to evaluate whether Audio-Language Models (LALMs) can infer the direction and trajectory of moving sound sources from binaural audio. Comprehensive quantitative and qualitative analyses reveal that current models struggle to reliably recognize motion cues or distinguish directional patterns. The average accuracy remains below 50%, underscoring a fundamental limitation in auditory spatial reasoning. Our study highlights a fundamental gap between human and model auditory spatial reasoning, providing both a diagnostic tool and new insight for enhancing spatial cognition in future Audio-Language Models.