SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding
Abstract
Large Audio-Language Models (ALMs) have recently demonstrated remarkable capabilities in holistic audio understanding, yet they remain unreliable for temporal grounding, i.e., the task of pinpointing exactly when an event occurs within long-form audio. This limitation stems from two factors: training data dominated by clip-level supervision lacking precise timestamps, and benchmarks that fail to simulate real-world scenarios where short events are obscured by dense background sounds. In this paper, we introduce SpotSound, an audio language model designed for grounding audio events. SpotSound incorporates a novel training objective, specifically designed to suppress hallucinated timestamps for events absent from the input. Additionally, we present SpotSound-Bench, a challenging temporal grounding benchmark where target events occupy less than ~10\% of each clip, creating a rigorous `needle-in-a-haystack' evaluation. Experiments demonstrate that SpotSound achieves state-of-the-art results on temporal grounding benchmarks while maintaining robust performance across general downstream audio-language tasks. Code, models and benchmark are released on https://loiesun.github.io/spotsound/
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 paper introduces SpotSound, a novel framework for enhancing large audio-language models with precise temporal grounding capabilities, addressing critical limitations in existing approaches and providing a new benchmark for evaluation.
Methodology
The methodology is robust, introducing a novel training objective that effectively suppresses hallucinations in temporal grounding tasks. The interleaving of timestamp tokens with audio tokens is a significant innovation that enhances temporal resolution. The two-stage problem formulation, separating event existence from temporal localization, is well-structured and addresses a critical gap in existing models. The synthetic dataset construction and the introduction of SpotSound-Bench as a benchmark are commendable contributions that enhance the paper's impact.
Results & evaluation
The experimental evaluation is comprehensive, demonstrating state-of-the-art performance across multiple benchmarks. The authors provide detailed comparisons with existing models, showcasing the effectiveness of their approach in both temporal grounding and sound event detection. The ablation studies further validate the contributions of various model components, enhancing the credibility of the results.
Reproducibility
The paper includes sufficient implementation details, including model architectures, training strategies, and dataset construction methods, which should facilitate reproducibility. However, the absence of a public code repository or demo limits immediate accessibility for other researchers.
Limitations
The model struggles with multi-instance scenarios where multiple occurrences of the same sound event are present, indicating potential limitations in its autoregressive decoding process. Additionally, the reliance on the quality of temporal annotations in the training data may affect generalization to more complex audio environments.
Broader impact
The advancements in temporal grounding have significant implications for real-world applications such as surveillance, media forensics, and interactive audio systems. By improving the ability of audio-language models to accurately localize events in complex auditory scenes, this work paves the way for more reliable audio understanding systems. The paper introduces SpotSound, a novel framework for enhancing large audio-language models with precise temporal grounding capabilities, addressing critical limitations in existing approaches and providing a new benchmark for evaluation.
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