Listening with Time: Precise Temporal Awareness for Long-Form Audio Understanding
Abstract
While Large Audio Language Models (LALMs) achieve strong performance on short audio, they degrade on long-form inputs. This degradation is more severe in temporal awareness tasks, where temporal alignment becomes increasingly inaccurate as audio duration grows. We attribute these limitations to the lack of data, benchmarks, and modeling approaches tailored for long-form temporal awareness. To bridge this gap, we first construct LAT-Chronicle, a 1.2k hour long-form audio dataset with temporal annotations across real-world scenarios. We further develop LAT-Bench, the first human-verified benchmark supporting audio up to 30 minutes while covering three core tasks: Dense Audio Caption, Temporal Audio Grounding, and Targeted Audio Caption. Leveraging these resources, we propose LAT-Audio, formulating temporal awareness as a progressive global-to-local reasoning paradigm. A global timeline is first constructed as an aligned temporal-semantic context,and the Think-With-Audio Chain-of-Thought (TWA-CoT) is then introduced to perform iterative reasoning by incorporating local audio information via tool use. Experiments show that LAT-Audio surpasses existing models on long-form audio temporal awareness tasks and improves robustness to input duration. We release the dataset, benchmark, and model to facilitate future research at https://github.com/alanshaoTT/LAT-Audio-Repo.
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 a novel framework and dataset for improving temporal awareness in long-form audio understanding, which significantly advances the state of the art in audio language models. The comprehensive methodology, robust experimental validation, and potential applications underscore its significance in the field of machine learning and audio processing.
Methodology
The paper presents a comprehensive methodology that addresses the limitations of existing Large Audio Language Models (LALMs) in handling long-form audio. The authors construct a new dataset (LAT-Chronicle) and benchmark (LAT-Bench) specifically designed for Long-form Audio Temporal Awareness (LATA) tasks, which include Dense Audio Captioning, Temporal Audio Grounding, and Targeted Audio Captioning. The proposed LAT-Audio framework introduces a novel global-to-local reasoning paradigm and the Think-With-Audio Chain-of-Thought (TWA-CoT) approach, which iteratively refines audio understanding by leveraging local audio segments based on a constructed global timeline. This innovative approach is well-justified and effectively addresses the challenges posed by long-form audio inputs.
Results & evaluation
The experimental evaluation is robust, demonstrating the effectiveness of LAT-Audio against existing models across multiple tasks. The authors provide thorough comparisons with baseline models and conduct ablation studies to validate the importance of key components such as the global timeline and TWA-CoT. The results show significant improvements in performance metrics, indicating that the proposed methods enhance temporal awareness and robustness in long-form audio understanding. The inclusion of a diverse dataset and human-verified benchmarks adds credibility to the findings.
Reproducibility
The paper includes detailed implementation details and a clear description of the training strategy, which enhances the reproducibility of the results. The authors provide access to the dataset, benchmark, and model through a GitHub repository, facilitating further research and validation of their findings by the community.
Limitations
While the proposed framework shows promise, there are limitations, such as the computational overhead introduced by multi-turn reasoning and tool use, which may hinder real-time applications. Additionally, the focus on single-audio inputs limits the framework's applicability in more complex multimodal scenarios. Future work is needed to enhance efficiency and extend the framework to broader contexts.
Broader impact
The research has significant implications for various applications, including automated transcription, audio search engines, and multimedia content analysis. By improving long-form audio understanding, the work can enhance user experiences in domains such as education, entertainment, and accessibility for the hearing impaired. The open-source nature of the project encourages further innovation and exploration in the field of audio language processing. The main contribution of this paper is the introduction of a novel framework and dataset for improving temporal awareness in long-form audio understanding, which significantly advances the state of the art in audio language models. The comprehensive methodology, robust experimental validation, and potential applications underscore its significance in the field of machine learning and audio processing.
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