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

2026-04-15 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Large Audio-Language Models (LALMs) excel at general audio understanding but struggle with fine-grained temporal perception, such as accurately identifying event onset and offset. To enhance this capability, researchers propose the TimePro-RL framework, which utilizes Audio-Side Time Prompts and Reinforcement Learning (RL). The method involves encoding timestamps as embeddings and interleaving them within the audio feature sequence, effectively serving as temporal coordinates to prompt the model. Following Supervised Fine-Tuning (SFT), RL is introduced to directly optimize the model's temporal alignment performance. Experiments confirm that TimePro-RL significantly improves performance across various audio temporal tasks, including audio grounding, sound event detection, and dense audio captioning, demonstrating its robust effectiveness.

Key takeaway

For research scientists developing or deploying Large Audio-Language Models, integrating Audio-Side Time Prompts and Reinforcement Learning via the TimePro-RL framework can significantly improve fine-grained temporal perception. You should consider this approach to enhance model accuracy in tasks requiring precise event onset/offset detection, such as audio grounding or dense audio captioning, thereby expanding the utility of your LALMs in complex audio analysis scenarios.

Key insights

TimePro-RL enhances LALMs' temporal perception by integrating timestamp embeddings and optimizing with reinforcement learning.

Principles

• Encode timestamps as temporal coordinates.
• Interleave temporal prompts within audio features.
• Optimize temporal alignment via RL post-SFT.

Method

Encode timestamps into embeddings, interleave these as temporal prompts within audio feature sequences, then apply Reinforcement Learning after Supervised Fine-Tuning to directly optimize temporal alignment performance.

In practice

• Improve audio grounding accuracy.
• Enhance sound event detection.
• Refine dense audio captioning.

Topics

• Large Audio-Language Models
• Temporal Perception
• Audio-Side Time Prompt
• Reinforcement Learning
• TimePro-RL

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt — TimePro-RL enhances LALMs' temporal perception by integrating explicit time prompts and optimizing with reinforcement learning.
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• Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models — Integrating trainable audio-side prompts with text-side prompt tuning significantly enhances few-shot adaptation in Audio-Language Models.
• Continuous Audio Thinking for Large Audio Language Models — CoAT enables LALMs to retain and organize acoustic information in a continuous latent space, improving understanding without added inference cost.
• Improving quality and robustness in LLM-based text-to-speech systems — LLM-based TTS quality improves through targeted techniques addressing accent leakage, expressiveness, and reliability.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.