Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt
Summary: arXiv:2604.13715v1 Announce Type: cross
Introduction
Large Audio-Language Models (LALMs) have emerged as a groundbreaking advancement in the field of audio understanding, showcasing exceptional capabilities across a spectrum of audio-related tasks. Despite their impressive performance, these models grapple with challenges associated with temporal perception, particularly in accurately inferring event onset and offset timings. This limitation hinders their effectiveness in applications requiring fine-grained temporal analysis, such as event detection and audio grounding.
The Proposed Solution: Audio-Side Time Prompt
To tackle the challenges faced in temporal perception, researchers have introduced the Audio-Side Time Prompt. This innovative approach incorporates a system of encoding timestamps as embeddings, which are then interwoven within the audio feature sequences. By integrating these temporal coordinates into the model’s input, the framework aims to enhance the model’s ability to understand and respond to the timing of audio events more accurately.
TimePro-RL Framework
Building upon the Audio-Side Time Prompt, the TimePro-RL framework employs Reinforcement Learning (RL) techniques for further optimization. The framework is designed to be applied after the Supervised Fine-Tuning (SFT) phase, targeting the direct improvement of temporal alignment performance. This combination allows the model to learn from both labeled data and feedback derived from its own performance metrics, resulting in a more refined understanding of temporal events.
Experimental Validation
The efficacy of the TimePro-RL framework has been validated through comprehensive experiments across a variety of audio temporal tasks. Key findings include:
- Audio Grounding: Enhanced accuracy in localizing sound events within audio streams.
- Sound Event Detection: Improved detection rates of specific audio events, contributing to better overall recognition performance.
- Dense Audio Captioning: More precise generation of captions that accurately reflect the temporal aspects of audio content.
These experiments demonstrate that the incorporation of the Audio-Side Time Prompt, coupled with the reinforcement learning approach, leads to substantial performance gains across the aforementioned audio temporal tasks.
Conclusion
The introduction of the Audio-Side Time Prompt and the TimePro-RL framework signifies a major step forward in addressing the temporal perception challenges faced by Large Audio-Language Models. By refining the model’s understanding of audio event timing, this approach not only enhances the utility of LALMs in fine-grained scenarios but also opens new avenues for research and application in the field of audio analysis. As the demand for accurate audio understanding continues to rise, advancements such as these will be crucial in shaping the future of audio technology.