SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models
Abstract
Spatial perception is central to auditory intelligence, enabling accurate understanding of real-world acoustic scenes and advancing human-level perception of the world around us. While recent large audio-language models (LALMs) show strong reasoning over complex audios, most operate on monaural inputs and lack the ability to capture spatial cues such as direction, elevation, and distance. We introduce SPUR, a lightweight, plug-in approach that equips LALMs with spatial perception through minimal architectural changes. SPUR consists of: (i) a First-Order Ambisonics (FOA) encoder that maps (W, X, Y, Z) channels to rotation-aware, listener-centric spatial features, integrated into target LALMs via a multimodal adapter; and (ii) SPUR-Set, a spatial QA dataset combining open-source FOA recordings with controlled simulations, emphasizing relative direction, elevation, distance, and overlap for supervised spatial reasoning. Fine-tuning our model on the SPUR-Set consistently improves spatial QA and multi-speaker attribution while preserving general audio understanding. SPUR provides a simple recipe that transforms monaural LALMs into spatially aware models. Extensive ablations validate the effectiveness of our approach.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of SPUR, a framework that effectively integrates spatial audio understanding into large audio-language models, enhancing their reasoning capabilities while preserving their general audio understanding. This work represents a significant step forward in bridging the gap between auditory scene understanding and high-level reasoning in machine learning.
Methodology
The proposed SPUR framework introduces a novel approach to integrating spatial audio understanding into large audio-language models (LALMs) through a lightweight, plug-and-play spatial adapter. The methodology is well-structured, utilizing a First-Order Ambisonics (FOA) encoder to extract spatial features and a multimodal adapter to condition existing LALMs. The detailed description of the four-stage pipeline for feature extraction and the innovative use of spatial covariance modeling are commendable. The approach's emphasis on maintaining the core model's integrity while enhancing spatial reasoning is a significant methodological strength.
Results & evaluation
The experiments conducted are thorough, with extensive ablation studies that validate the effectiveness of the SPUR framework. The introduction of the SPUR-Set dataset is a notable contribution, providing a robust benchmark for evaluating spatial reasoning in audio-language models. The results demonstrate significant improvements in spatial QA and multi-speaker attribution tasks, showcasing the practical impact of the proposed method. The comparison against existing models further strengthens the evaluation, although it would benefit from more diverse baseline comparisons.
Reproducibility
The paper provides a clear outline of the experimental setup, including hyperparameters and training protocols, which enhances reproducibility. However, the absence of a publicly available code repository limits the ability for external validation of results. Future work should consider releasing the implementation to foster community engagement and further research.
Limitations
The paper acknowledges several limitations, including the reliance on FOA, which may constrain spatial resolution and directionality. The dataset's focus on controlled environments may also limit generalization to real-world scenarios. Additionally, the static nature of the embeddings does not account for dynamic listener movement, which could be a significant factor in spatial audio applications.
Broader impact
The SPUR framework has the potential to advance spatial audio understanding significantly, with applications in immersive media, AR/VR, and assistive technologies for individuals with visual impairments. By enabling more nuanced spatial reasoning in audio-language models, it could enhance user interaction in various contexts, from creative workflows to navigation in complex auditory environments. The main contribution of this paper is the introduction of SPUR, a framework that effectively integrates spatial audio understanding into large audio-language models, enhancing their reasoning capabilities while preserving their general audio understanding. This work represents a significant step forward in bridging the gap between auditory scene understanding and high-level reasoning in machine learning.
Author of this paper? Send a correction.