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Listening Between the Frames: Bridging Temporal Gaps in Large Audio-Language Models

Hualei Wang, Yiming Li, Shuo Ma, Hong Liu, Xiangdong Wang

arXiv:2511.11039v2cs.SD

TL;DR

TimeAudio addresses LALMs’ difficulty with precise temporal localization and long-audio understanding by adding time-sensitive modeling and segment-level token merging, alongside the FTAR dataset. The authors report improved downstream temporal-task performance while retaining general audio comprehension.

Abstract

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Recent Large Audio-Language Models (LALMs) exhibit impressive capabilities in understanding audio content for conversational QA tasks. However, these models struggle to accurately understand timestamps for temporal localization (e.g., Temporal Audio Grounding) and are restricted to short audio perception, leading to constrained capabilities on fine-grained tasks. We identify three key aspects that limit their temporal localization and long audio understanding: (i) timestamp representation, (ii) architecture, and (iii) data. To address this, we introduce TimeAudio, a novel method that empowers LALMs to connect their understanding of audio content with precise temporal perception. Specifically, we incorporate unique temporal markers to improve time-sensitive reasoning and apply an absolute time-aware encoding that explicitly grounds the acoustic features with absolute time information. Moreover, to achieve end-to-end long audio understanding, we introduce a segment-level token merging module to substantially reduce audio token redundancy and enhance the efficiency of information extraction. Due to the lack of suitable datasets and evaluation metrics, we consolidate existing audio datasets into a new dataset focused on temporal tasks and establish a series of metrics to evaluate the fine-grained performance. Evaluations show strong performance across a variety of fine-grained tasks, such as dense captioning, temporal grounding, and timeline speech summarization, demonstrating TimeAudio's robust temporal localization and reasoning capabilities.

Introduction

Large audio-language models support flexible audio understanding, but precise temporal localization and long-form comprehension remain difficult. TimeAudio combines temporal modeling modules with a task-focused dataset to improve fine-grained understanding.

Introduction

  • LALMs integrate audio encoders into pretrained decoder-based LLMs to support free-form audio question answering and unified audio understanding.Earlier systems were limited to fixed task formulations or exclusive audio types.Source: Introduction · p. 1
  • LALMs struggle to connect acoustic semantics with precise timestamps and hallucinate on long audio, with direct feature projection and general-purpose tuning and benchmarks identified as possible causes.Figure 1 illustrates failures on tasks requiring both semantics and timestamps.Source: Figure 1 · p. 1, Introduction · p. 2
  • TimeAudio combines temporal markers, absolute time-aware encoding, and token selection and merging with the FTAR instruction dataset for fine-grained audio understanding.The paper also introduces metrics for evaluating fine-grained performance.Source: Introduction · p. 2

Related Work

Fine-grained temporal understanding links audio semantics to time intervals, but current LALMs and available datasets have important gaps. Prior long-audio approaches use specialized adaptations that are not directly transferable.

Fine-grained Temporal Audio Understanding

  • Fine-grained temporal understanding pairs semantics with time intervals, offering more traceable evidence and details that matter for reducing hallucination and building responsible models.Current LALMs perform poorly at temporal reasoning, expert models lack zero-shot capability, and existing datasets lack suitable instruction data and are highly imbalanced.Source: Fine-grained Temporal Audio… · p. 2

Large Audio Language Models

  • Long-input LALMs often hallucinate; Audio Flamingo2 uses sliding windows and CLAP, while Qwen2.5-Omni uses streaming transcription, adaptations described as not directly transferable.The passage attributes that boundary to extensive training or specialized architecture adaptations.Source: Large Audio Language Models · p. 2

Method

TimeAudio combines temporal markers, absolute time-aware encoding, and segment-level token merging with a temporal instruction dataset to support fine-grained temporal reasoning and long-audio understanding.

Overview of TimeAudio

  • The overview splits audio into segments, encodes them, projects tokens into language space, merges segment-level tokens, and represents timestamps with anchor and offset tokens.The sliding encoder combines speech and audio features; the LLM receives the resulting audio embeddings together with the user prompt.Source: Figure 2 · p. 3, Overview of TimeAudio · p. 3

Temporal Markers

  • Temporal markers convert continuous timestamps into anchor tokens and fine-grained offset tokens, keeping precision constant regardless of audio length.This design addresses challenges from predicting timestamps as numbers and from fixed-interval relative tokens.Source: Temporal Markers · p. 3

Absolute Time-aware Encoding

  • Absolute time-aware encoding adds learnable timestamp embeddings to acoustic features, grounding each feature at its position on the audio timeline.The time embedding uses a discretized timestamp index, and is zero-initialized to preserve pretrained audio encoders early in training.Source: Absolute Time-aware Encoding · p. 4

Segment-level Token Merging

  • Attention-ranked tokens are retained within each segment, while remaining tokens are clustered by key-vector similarity and fused into contextual tokens.The method reuses Q-former attention information to avoid extra computation and memory overhead.Source: Segment-level Token Merging · p. 4

Training Strategy

  • Training first aligns fine-grained audio features with temporal information, then tunes the window Q-former and LoRA on instruction data for long-audio understanding.Stage one freezes the audio encoder and LLM; stage two freezes all components except the window Q-former and LoRA.Source: Training Strategy · pp. 4–5

FTAR Dataset Construction

  • FTAR contains 260K public audio-text pairs spanning dense captioning, temporal grounding, and timeline speech summarization, with temporal QA and general audio data also included.Its construction draws on datasets for event captions, event localization, and speech summarization, as well as OpenAQA for temporal questions and answers.Source: FTAR Dataset Construction · p. 5

Experiments

Experiments compare TimeAudio with general-purpose and fine-tuned LALMs across three temporal tasks, then use ablations and qualitative examples to examine its modules and performance.

Evaluation Setups

  • Evaluation spans AudioSet-Strong dense captioning, AudioGrounding localization, and a CNN/DailyMail subset for speech summaries, comparing zero-shot general LALMs and fine-tuned task-adapted models.The speech-summary subset includes articles under 1,600 characters.Source: Evaluation Setups · pp. 5–6

Main Results

  • 57.8 mIoU on AudioGrounding surpasses fine-tuned SALMONN-7B by an 11.4% mIoU gain, with the largest reported gains on temporal grounding.The comparison identifies temporal grounding as the task with TimeAudio’s greatest performance gains.Source: Table 2 · p. 6, Main Results · p. 6
  • TimeAudio gains +0.9 Eb-F1 and +2.7 At-F1 over fine-tuned Qwen2-Audio, while that baseline scores stronger on METEOR.The authors speculate that the METEOR result reflects the initialization checkpoint’s limited audio-captioning diversity.Source: Main Results · p. 6, Table 2 · p. 6
  • TimeAudio reaches 42.4 ROUGE-1 and 30.8 ROUGE-L on CNN/DailyMail speech, outperforming other LALMs with speech-summarization capabilities and Hubert-MiniChat.The authors attribute the result to segment-level token merging retaining important semantics from each audio segment.Source: Main Results · p. 6, Table 2 · p. 6

Ablation Study

  • TM-only yields +3.6 Eb-F1, +3.0 mIoU, and +4.4 mIoU on AudioSet-Strong, AudioGrounding, and CNN/DailyMail, respectively.Adding TM and ATE increases METEOR by 0.9 and Eb-F1 by 5.4.Source: Ablation Study · p. 6, Table 3 · p. 7
  • Segment-level token merging improves timeline-speech ROUGE-1 and mIoU but moderately reduces dense-captioning precision.The authors report that it preserves more semantics in long-form audio and improves alignment between summaries and audio content.Source: Ablation Study · pp. 6–7, Table 3 · p. 7
  • Lower attentive-token retention underperforms on both aspects, while gains from increasing token count level off; the selected ratio is 0.25.The authors choose this ratio as a balance between performance and computational efficiency.Source: Ablation Study · p. 7, Table 4 · p. 7

Qualitative Performance

  • Figure 4 marks intervals yellow and audio content red; TimeAudio captures event boundaries more precisely than Qwen2-Audio, whose short context prompts guessing on long speech.Qwen2-Audio tends to predict delayed start times and misses complete event intervals in the short-audio example.Source: Figure 4 · p. 7, Qualitative Performance · p. 7

Conclusion

TimeAudio combines temporal modeling, long-audio token merging, and the FTAR dataset for fine-grained audio understanding. It improves time-centric task performance over baselines while retaining general audio comprehension.

Conclusion

  • TimeAudio improves performance over baselines on downstream temporal audio tasks while retaining general audio comprehension.The conclusion reports extensive experiments across time-centric scenarios.Source: Conclusion · p. 7
  • Temporal markers and absolute time-aware encoding support temporal modeling, while segment-level merging compresses long audio while preserving dominant information.The method also uses the FTAR dataset to strengthen temporal reasoning.Source: Conclusion · p. 7
  • The results support treating time as a crucial dimension for fine-grained audio perception.Source: Conclusion · p. 7

Appendix A: FTAR Dataset Construction

FTAR combines four long timestamp-related audio tasks with datasets from different domains. Its construction covers task-specific data, instruction generation, and answer refinement and curation.

Appendix A: FTAR Dataset Construction

  • FTAR combines four long timestamp-related tasks and eight datasets from different domains.Table 6 presents task instructions, output formats, and expected responses.Source: Appendix A · p. 10
  • Dense audio captioning requires detecting events and returning timestamped descriptions, using AudioSet-Strong, TACOS, and AudioTime.AudioSet-Strong has 97,315 annotated audio samples; TACOS has 12,358 recordings, and AudioTime has 5,000 samples.Source: Appendix A · p. 10
  • Temporal audio grounding maps a natural-language query to its start and end times, drawing on AudioSet-Strong and AudioGrounding.AudioGrounding contains 4,662 samples with human-annotated phrase onsets and offsets.Source: Appendix A · p. 10
  • Timeline speech summarization preserves key spoken content with temporal context, using synthesized CNN/DailyMail speech and merged Librispeech segments.The CNN/DailyMail-derived set contains 43,018 samples.Source: Appendix A · p. 10
  • Audio Temporal Question Answering tests reasoning about counting, duration, and time sequences using 15,000 OpenAQA samples.The task targets free-form, open-ended questions, including those about instructional audio.Source: Appendix A · p. 10
  • Construction uses six manually selected instructions per task, Qwen2.5-refined event descriptions, and manual collection and curation of the constituent datasets.Gemini 2.5 generates instruction variants from manually written seeds; timestamps follow the user prompt, and captions are instructed to preserve time ranges.Source: Appendix A · p. 10

Appendix B:Hyper-parameters for Instruction Tuning

The appendix points to a table of instruction-tuning hyperparameters and provides examples of task instructions, output formats, and outputs.

Appendix B:Hyper-parameters for Instruction Tuning

  • Table 5 lists the hyperparameter settings used for fine-tuning.Source: Appendix B · p. 10
  • Grounding examples ask for query start and end times and show outputs as timestamped intervals.One example returns three intervals: 0.2–2.7, 3.3–5.7, and 6.5–7.2 seconds.Source: Appendix B · p. 11
  • Timeline summarization examples request key points at specific intervals and format outputs as time-bounded summaries.The example divides a 120.5-second speech sample into three segments.Source: Appendix B · p. 11
  • Other examples ask about background-noise duration and infer the number of singers from event timing.The sample answer infers that five men are singing.Source: Appendix B · p. 11

Appendix C: Details of Evaluation Datasets and Metrics

The evaluation assesses zero-shot generalization on dense audio captioning, temporal audio grounding, and timeline speech summarization. It uses task-specific datasets and metrics, with qualitative examples for each task.

Appendix C: Details of Evaluation Datasets and Metrics

  • Zero-shot generalization is assessed on dense captioning, temporal grounding, and timeline speech summarization.Source: Appendix C · p. 11
  • Dense captioning uses 770 AudioSet-Strong samples, METEOR for caption quality, and event-based and clip-level macro F1 measures for localization and description diversity.The audios average 10 seconds and have six temporally localized imperative-sentence annotations; Figure 5 presents qualitative results.Source: Appendix C · p. 11, Figure 5 · p. 12
  • Temporal grounding evaluates predicted boundaries with mean IoU and Recall at 0.5, 0.7, and 0.9 on AudioGrounding test data.The test set has 997 queries; Figure 6 presents qualitative results.Source: Appendix C · p. 11, Figure 6 · p. 12
  • Timeline speech summarization reports ROUGE, METEOR, and mIoU on over 886 CNN/DailyMail speech samples; Figure 7 presents qualitative results.The mIoU measures how accurately summaries are grounded to the audio timeline.Source: Appendix C · pp. 11–12, Figure 7 · p. 12

Appendix D: More Qualitative Results

Figures 5–7 provide qualitative examples of TimeAudio across dense audio captioning, temporal audio grounding, and timeline speech summarization, illustrating its handling of diverse temporal localization tasks.

Appendix D: More Qualitative Results

  • Figures 5–7 present an extended set of qualitative results for TimeAudio.The examples cover dense audio captioning, temporal audio grounding, and timeline speech summarization.Source: Appendix D · p. 12
  • The examples span dense audio captioning, temporal audio grounding, and timeline speech summarization.Source: Appendix D · p. 12
  • The examples highlight the model’s ability to handle diverse complex temporal localization tasks.Source: Appendix D · p. 12
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