Augmenting Large Audio-Language Models with Frame-Level Grounding for Fine-Grained Temporal Perception
Abstract
Large Audio-Language Models (LALMs) have substantially advanced general audio understanding, yet they remain limited in fine-grained temporal perception, particularly in precise event localization. Existing approaches primarily post-train LALMs to predict event boundaries as timestamp tokens. However, this generative formulation lacks explicit correspondence between the timestamp predictions and fine-grained acoustic evidence, limiting the precision and reliability of temporal localization. To address this issue, we augment the LALM with a dedicated frame-level grounding model while leveraging its semantic modeling capability to represent the event query. Specifically, the frozen LALM encodes the event query with audio as context, and the grounding model combines these query representations with fine-grained audio features to localize the target event at the frame level. Extensive experiments across diverse temporal grounding benchmarks demonstrate strong and consistent improvements over existing methods. Further evaluation shows that the grounding model can provide temporal evidence to support downstream reasoning.
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 proposes a modular framework that augments frozen Large Audio-Language Models with a dedicated frame-level grounding model to achieve precise temporal localization. By decoupling semantic query representation from acoustic event detection and using cross-attention for frame-level alignment, the method significantly outperforms existing generative and specialized baselines across multiple benchmarks, while also enhancing downstream temporal reasoning capabilities when used as an external tool.
Methodology
The paper proposes a modular framework that decouples semantic query understanding from fine-grained temporal localization. Instead of post-training the Large Audio-Language Model (LALM) to generate timestamp tokens (which is error-prone and lacks explicit acoustic grounding), the authors freeze the LALM and use its hidden states as semantic queries. A separate, lightweight grounding model uses cross-attention to align these semantic queries with frame-level audio features from a specialized encoder (ATST-Frame) and a CNN branch. This allows the model to predict frame-level activity scores directly, which are then converted to temporal intervals. The methodology is sound, leveraging the strengths of LALMs (semantic reasoning) and specialized audio encoders (temporal precision) while mitigating their respective weaknesses. The addition of a "tool-use" mechanism for downstream reasoning is a clever architectural choice that enhances the utility of the grounding model.
Results & evaluation
The experimental evaluation is comprehensive, covering five diverse benchmarks (AudioGrounding, DESED, UnAV-100, TACOS, Clotho-Moment). The results show consistent and significant improvements over strong baselines, including specialized grounding models (SpotSound, AM-DETR) and recent LALMs (Qwen3-Omni, Audio Flamingo 3). The gains are particularly notable at stricter IoU thresholds ([email protected]), validating the claim of improved precision. The ablation studies effectively isolate the contributions of audio context in the LALM, the choice of audio encoder, and the frame-level alignment mechanism. The downstream reasoning experiments on DCASE 2025 Task 5 further demonstrate the practical value of the grounding model as a tool for temporal reasoning.
Reproducibility
The paper provides sufficient implementation details for reproducibility, including specific hyperparameters (learning rate, batch size, number of epochs, attention heads), architectural choices (Conformer decoder, CNN branch), and preprocessing steps (10s windows, 50Hz upsampling). The use of publicly available checkpoints for baselines and open-source models for the backbone (Qwen3-Omni, Audio Flamingo 3) facilitates reproduction. However, the specific code repository is not linked in the provided text, which is a minor drawback.
Limitations
The framework relies on a frozen LALM, which means it cannot adapt to new semantic concepts or out-of-distribution queries without retraining the grounding model's alignment. The 10-second windowing strategy may limit performance on very long-form audio or events spanning multiple windows. The evaluation is limited to English audio and specific benchmarks; generalization to other languages or more complex, multi-event scenarios is not fully explored. The "tool-use" reasoning approach adds inference latency and complexity.
Broader impact
This work addresses a critical bottleneck in audio AI: the lack of precise temporal grounding in large multimodal models. By providing a modular solution that can be attached to existing LALMs, it offers a practical path for enhancing temporal perception in audio understanding systems. This has implications for applications requiring precise event localization, such as video editing, audio surveillance, and interactive audio assistants. The demonstrated improvement in downstream reasoning suggests that explicit temporal evidence can significantly boost the reliability of LALM-based reasoning tasks. The paper proposes a modular framework that augments frozen Large Audio-Language Models with a dedicated frame-level grounding model to achieve precise temporal localization. By decoupling semantic query representation from acoustic event detection and using cross-attention for frame-level alignment, the method significantly outperforms existing generative and specialized baselines across multiple benchmarks, while also enhancing downstream temporal reasoning capabilities when used as an external tool.
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