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Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval

Shunsuke Tsubaki, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, Keisuke Imoto

TL;DR

The paper addresses the scarcity of paired audio-text data by leveraging abundant audio-image data to improve audio-text cross retrieval. It introduces two refined audio-image pretraining schemes, Nearest Match and Multiframe Match, to better capture temporal alignment between audio and frames within videos, built on a CLIP-based image-text backbone and InfoNCE contrastive learning. Experiments on AudioSet and AudioCaps show that Nearest Match enhances audio-text retrieval, particularly with longer training, while Multiframe Match substantially improves audio-image retrieval, indicating complementary benefits for cross-modal transfer. The findings demonstrate that fine-grained temporal alignment in the audio-image stage can boost downstream audio-text retrieval, with potential for deeper data-mismatch analysis in future work.

Abstract

The aim of this research is to refine knowledge transfer on audio-image temporal agreement for audio-text cross retrieval. To address the limited availability of paired non-speech audio-text data, learning methods for transferring the knowledge acquired from a large amount of paired audio-image data to shared audio-text representation have been investigated, suggesting the importance of how audio-image co-occurrence is learned. Conventional approaches in audio-image learning assign a single image randomly selected from the corresponding video stream to the entire audio clip, assuming their co-occurrence. However, this method may not accurately capture the temporal agreement between the target audio and image because a single image can only represent a snapshot of a scene, though the target audio changes from moment to moment. To address this problem, we propose two methods for audio and image matching that effectively capture the temporal information: (i) Nearest Match wherein an image is selected from multiple time frames based on similarity with audio, and (ii) Multiframe Match wherein audio and image pairs of multiple time frames are used. Experimental results show that method (i) improves the audio-text retrieval performance by selecting the nearest image that aligns with the audio information and transferring the learned knowledge. Conversely, method (ii) improves the performance of audio-image retrieval while not showing significant improvements in audio-text retrieval performance. These results indicate that refining audio-image temporal agreement may contribute to better knowledge transfer to audio-text retrieval.

Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval

TL;DR

The paper addresses the scarcity of paired audio-text data by leveraging abundant audio-image data to improve audio-text cross retrieval. It introduces two refined audio-image pretraining schemes, Nearest Match and Multiframe Match, to better capture temporal alignment between audio and frames within videos, built on a CLIP-based image-text backbone and InfoNCE contrastive learning. Experiments on AudioSet and AudioCaps show that Nearest Match enhances audio-text retrieval, particularly with longer training, while Multiframe Match substantially improves audio-image retrieval, indicating complementary benefits for cross-modal transfer. The findings demonstrate that fine-grained temporal alignment in the audio-image stage can boost downstream audio-text retrieval, with potential for deeper data-mismatch analysis in future work.

Abstract

The aim of this research is to refine knowledge transfer on audio-image temporal agreement for audio-text cross retrieval. To address the limited availability of paired non-speech audio-text data, learning methods for transferring the knowledge acquired from a large amount of paired audio-image data to shared audio-text representation have been investigated, suggesting the importance of how audio-image co-occurrence is learned. Conventional approaches in audio-image learning assign a single image randomly selected from the corresponding video stream to the entire audio clip, assuming their co-occurrence. However, this method may not accurately capture the temporal agreement between the target audio and image because a single image can only represent a snapshot of a scene, though the target audio changes from moment to moment. To address this problem, we propose two methods for audio and image matching that effectively capture the temporal information: (i) Nearest Match wherein an image is selected from multiple time frames based on similarity with audio, and (ii) Multiframe Match wherein audio and image pairs of multiple time frames are used. Experimental results show that method (i) improves the audio-text retrieval performance by selecting the nearest image that aligns with the audio information and transferring the learned knowledge. Conversely, method (ii) improves the performance of audio-image retrieval while not showing significant improvements in audio-text retrieval performance. These results indicate that refining audio-image temporal agreement may contribute to better knowledge transfer to audio-text retrieval.
Paper Structure (15 sections, 5 equations, 2 figures, 3 tables)

This paper contains 15 sections, 5 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Image-text pretraining
  4. Audio-image pretraining (conventional)
  5. Refined audio-image pretraining (proposal)
  6. Nearest Match
  7. Multiframe Match
  8. Audio-text fine-tuning
  9. Experiments
  10. Experimental conditions
  11. Implementation details
  12. Dataset
  13. Evaluation metrics
  14. Results and Discussion
  15. Conclusion

Figures (2)

  • Figure 1: Contrastive audio-image pretraining and audio-text fine-tuning and diagram of the embedding space. The image, audio, and text are colored blue, pink, and green, respectively.
  • Figure 2: Overview of our proposed audio-image pretraining scheme. On the left, the method is based on Nearest Match. On the right, the method is based on Multiframe Match. $B$ is the batch size during training.