Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt

Yanfeng Shi, Pengfei Cai, Jun Liu ...Full author list in the original paper.

Singapore Institute of Technology +2 · arXiv

Affiliations: National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China, Singapore Institute of Technology

Paper

Abstract

Large Audio-Language Models (LALMs) enable general audio understanding and demonstrate remarkable performance across various audio tasks. However, these models still face challenges in temporal perception (e.g., inferring event onset and offset), leading to limited utility in fine-grained scenarios. To address this issue, we propose Audio-Side Time Prompt and leverage Reinforcement Learning (RL) to develop the TimePro-RL framework for fine-grained temporal perception. Specifically, we encode timestamps as embeddings and interleave them within the audio feature sequence as temporal coordinates to prompt the model. Furthermore, we introduce RL following Supervised Fine-Tuning (SFT) to directly optimize temporal alignment performance. Experiments demonstrate that TimePro-RL achieves significant performance gains across a range of audio temporal tasks, such as audio grounding, sound event detection, and dense audio captioning, validating its robust effectiveness.

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 the TimePro-RL framework, which significantly enhances the temporal perception capabilities of Large Audio-Language Models through innovative methodologies and experimental validation. This work represents a meaningful step forward in addressing the challenges of fine-grained audio understanding, with the potential to impact a wide range of applications in audio processing and multimodal machine learning.

Methodology

The proposed TimePro-RL framework introduces a novel approach to enhance temporal perception in Large Audio-Language Models (LALMs) by interleaving timestamp embeddings into audio feature sequences. This methodology effectively integrates Reinforcement Learning (RL) post-training to optimize temporal alignment, which is a significant advancement over traditional supervised fine-tuning (SFT) methods. The use of Audio-Side Time Prompt (ASTP) to inject temporal cues is particularly innovative, as it addresses the limitations of existing models in capturing fine-grained temporal structures. The combination of semantic initialization for timestamp embeddings and an adaptive reward mechanism in RL demonstrates a thoughtful and systematic approach to improving model performance.

Results & evaluation

The experiments conducted across three audio temporal tasks (Audio Grounding, Sound Event Detection, and Dense Audio Captioning) provide a comprehensive evaluation of the proposed framework. The results show significant performance improvements over baseline models, indicating the effectiveness of the TimePro-RL framework. The use of multiple evaluation metrics, including recall values and F1 scores, allows for a robust assessment of model capabilities. However, the paper could benefit from a more detailed discussion of the datasets used and potential biases in the evaluation process.

Reproducibility

The paper provides a clear description of the implementation details, including the architecture of the models used, the training process, and the specific hyperparameters. However, the lack of a publicly available code repository or demo URL limits the reproducibility of the results. Future work should consider making the code and models accessible to facilitate validation of the findings by the research community.

Limitations

One limitation of the study is the reliance on specific datasets, which may not fully represent the diversity of audio events encountered in real-world applications. Additionally, while the proposed framework shows improvements, the potential for overfitting during the RL training phase should be addressed. The paper also does not explore the scalability of the approach to larger datasets or more complex audio scenarios.

Broader impact

The advancements in fine-grained temporal perception for audio tasks have significant implications for various applications, including multimedia content analysis, automated audio tagging, and interactive audio systems. The ability to accurately localize sound events and generate contextually relevant audio captions can enhance user experiences in areas such as virtual reality, accessibility technologies, and intelligent personal assistants. The integration of RL in training models also opens avenues for improved performance in other multimodal tasks. The main contribution of this paper is the introduction of the TimePro-RL framework, which significantly enhances the temporal perception capabilities of Large Audio-Language Models through innovative methodologies and experimental validation. This work represents a meaningful step forward in addressing the challenges of fine-grained audio understanding, with the potential to impact a wide range of applications in audio processing and multimodal machine learning.

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