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Enhance Temporal Relations in Audio Captioning with Sound Event Detection

Zeyu Xie, Xuenan Xu, Mengyue Wu, Kai Yu

TL;DR

Problem: AAC systems struggle to describe temporal relations between audio events. Approach: introduces a temporal tag-guided captioning model, temp-tag-AAC, that derives four-scale temporal tags from SED outputs and uses them as guidance at the first decoding step; compares against a baseline and direct SED integration methods (Cat-prob-AAC, Attn-prob-AAC). Contributions: (i) a 4-scale temporal tag system with a matching mechanism to align SED-derived relations with temporal conjunctions, (ii) new temporal metrics $ACC_{temp}$ and $F1_{temp}$, and (iii) empirical evidence that temp-tag-AAC substantially improves temporal expression accuracy on AudioCaps and generalizes less well on Clotho due to domain mismatch. Findings: direct SED fusion yields limited gains in temporal descriptions, while temp-tag-AAC improves $ACC_{temp}$ and $F1_{temp}$ and maintains competitive overall caption quality on AudioCaps. Significance: enables more human-like temporal reasoning in AAC, with potential to improve user understanding of audio content.

Abstract

Automated audio captioning aims at generating natural language descriptions for given audio clips, not only detecting and classifying sounds, but also summarizing the relationships between audio events. Recent research advances in audio captioning have introduced additional guidance to improve the accuracy of audio events in generated sentences. However, temporal relations between audio events have received little attention while revealing complex relations is a key component in summarizing audio content. Therefore, this paper aims to better capture temporal relationships in caption generation with sound event detection (SED), a task that locates events' timestamps. We investigate the best approach to integrate temporal information in a captioning model and propose a temporal tag system to transform the timestamps into comprehensible relations. Results evaluated by the proposed temporal metrics suggest that great improvement is achieved in terms of temporal relation generation.

Enhance Temporal Relations in Audio Captioning with Sound Event Detection

TL;DR

Problem: AAC systems struggle to describe temporal relations between audio events. Approach: introduces a temporal tag-guided captioning model, temp-tag-AAC, that derives four-scale temporal tags from SED outputs and uses them as guidance at the first decoding step; compares against a baseline and direct SED integration methods (Cat-prob-AAC, Attn-prob-AAC). Contributions: (i) a 4-scale temporal tag system with a matching mechanism to align SED-derived relations with temporal conjunctions, (ii) new temporal metrics and , and (iii) empirical evidence that temp-tag-AAC substantially improves temporal expression accuracy on AudioCaps and generalizes less well on Clotho due to domain mismatch. Findings: direct SED fusion yields limited gains in temporal descriptions, while temp-tag-AAC improves and and maintains competitive overall caption quality on AudioCaps. Significance: enables more human-like temporal reasoning in AAC, with potential to improve user understanding of audio content.

Abstract

Automated audio captioning aims at generating natural language descriptions for given audio clips, not only detecting and classifying sounds, but also summarizing the relationships between audio events. Recent research advances in audio captioning have introduced additional guidance to improve the accuracy of audio events in generated sentences. However, temporal relations between audio events have received little attention while revealing complex relations is a key component in summarizing audio content. Therefore, this paper aims to better capture temporal relationships in caption generation with sound event detection (SED), a task that locates events' timestamps. We investigate the best approach to integrate temporal information in a captioning model and propose a temporal tag system to transform the timestamps into comprehensible relations. Results evaluated by the proposed temporal metrics suggest that great improvement is achieved in terms of temporal relation generation.
Paper Structure (15 sections, 4 equations, 3 figures, 2 tables, 1 algorithm)

This paper contains 15 sections, 4 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Temporal-Enhanced Captioning System
  3. Baseline Approach
  4. SED Architecture
  5. Direct SED Integration
  6. Temp-tag-AAC
  7. Experimental Setup
  8. Datasets
  9. Hyper-parameters
  10. Metrics
  11. Results and Analysis
  12. Temporal Relation Enhancement
  13. Overall Quality of Generated Sentences
  14. Comparison Between Different Approaches
  15. Conclusions

Figures (3)

  • Figure 1: Expressions of relationships in image versus audio. Image pays more attention to spatial relations while audio focus on temporal relations.
  • Figure 2: An overview of different AAC models. (A) Baseline AAC model: the decoder generates captions solely based on audio embeddings; (B) Cat-prob-AAC: audio embeddings and SED outputs are concatenated and used as the input to the decoder; (C) Attn-prob-AAC: an attention mechanism is used to integrate SED outputs and decoder hidden states; (D) Temp-tag-AAC: mimicking human judgment, tags are extracted and used as the input at the first timestep instead of $<$BOS$>$.
  • Figure 3: Output examples generated by baseline systems and tag guided approach.