Frame-Level Internal Tool Use for Temporal Grounding in Audio LMs
Abstract
Large audio language models are increasingly used for complex audio understanding tasks, but they struggle with temporal tasks that require precise temporal grounding, such as word alignment and speaker diarization. The standard approach, where we generate timestamps as sequences of text tokens, is computationally expensive and prone to hallucination, especially when processing audio lengths outside the model's training distribution. In this work, we propose frame-level internal tool use, a method that trains audio LMs to use their own internal audio representations to perform temporal grounding directly. We introduce a lightweight prediction mechanism trained via two objectives: a binary frame classifier and a novel inhomogeneous Poisson process (IHP) loss that models temporal event intensity. Across word localization, speaker diarization, and event localization tasks, our approach outperforms token-based baselines. Most notably, it achieves a >50x inference speedup and demonstrates robust length generalization, maintaining high accuracy on out-of-distribution audio durations where standard token-based models collapse completely.
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 frame-level internal tool use for temporal grounding in audio language models, which significantly enhances performance and efficiency in audio understanding tasks. The innovative methodology and promising experimental results position this work as a meaningful advancement in the field of machine learning, particularly in audio processing.
Methodology
The proposed methodology of frame-level internal tool use is innovative as it shifts the focus from token-based timestamp generation to leveraging internal audio representations for temporal grounding. The introduction of a binary frame classifier and the inhomogeneous Poisson process (IHP) loss function are significant advancements that enhance the model's ability to handle temporal tasks more efficiently. The dual-objective training approach is well-articulated and appears to be a robust framework for improving the precision of audio language models in temporal grounding tasks.
Results & evaluation
The experimental setup is comprehensive, covering various tasks such as word localization, speaker diarization, and event localization. The results indicate a substantial performance improvement over token-based baselines, particularly in terms of inference speed and robustness to out-of-distribution audio lengths. However, the paper could benefit from a more detailed comparison with existing state-of-the-art methods to better contextualize its contributions.
Reproducibility
The paper lacks explicit details on implementation and hyperparameters, which are only briefly mentioned in the appendix. For reproducibility, it would be beneficial to provide a public code repository or detailed instructions on how to replicate the experiments. The absence of a demo URL further limits the accessibility of the proposed methods for the research community.
Limitations
One limitation is the reliance on internal audio representations, which may not generalize well across all audio types or tasks. Additionally, while the paper claims significant speed improvements, it does not provide a thorough analysis of the trade-offs between speed and accuracy, particularly in edge cases. The lack of a clear evaluation of the model's performance under various noise conditions is also a concern.
Broader impact
The implications of this research are significant for applications in audio processing, such as automated transcription services, real-time speaker recognition systems, and enhanced accessibility tools for the hearing impaired. By improving the efficiency and accuracy of temporal grounding in audio tasks, this work can contribute to advancements in human-computer interaction and multimedia content analysis. The main contribution of this paper is the introduction of frame-level internal tool use for temporal grounding in audio language models, which significantly enhances performance and efficiency in audio understanding tasks. The innovative methodology and promising experimental results position this work as a meaningful advancement in the field of machine learning, particularly in audio processing.
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