Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval
<|reference_start|>Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval: 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.<|reference_end|>
@article{tsubaki2024refining,
    title={Refining Knowledge Transfer on Audio-Image Temporal Agreement for
  Audio-Text Cross Retrieval},
    author={Shunsuke Tsubaki, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi,
  Noboru Harada, Keisuke Imoto},
    journal={arXiv preprint arXiv:2403.10756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10756},
    primaryClass={eess.AS cs.SD}
}