mlsanityData to 14 Oct 2026

CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

arXiv 2501.09877 ↗First posted 16 Jan 2025eess.AS cs.LGcited by 1

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Abstract (arXiv)

Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiber-optic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory-recorded fiber-optic ESC-50 datasets and a real-world fiber-optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks. The code and gunshot-firework dataset are available at https://github.com/Jingchensun/clap-s.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 16 Jan 2025.

Builds on

15 of 32 references.

References · 15

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  2. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
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  11. 2023Audio-free Prompt Tuning for Language-Audio Modelscited by 13
  12. 2023Prompt Tuning based Adapter for Vision-Language Model Adaptioncited by 10
3 more
  1. 2023Adapting Language-Audio Models as Few-Shot Audio Learnerscited by 10
  2. 2024PALM: Few-Shot Prompt Learning for Audio Language Modelscited by 8
  3. 2023Low-rank constrained multichannel signal denoising considering channel-dependent sensitivity inspired by self-supervised learning for optical fiber sensingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Drift-Augmented Scoring: Text-Derived Noise Robustness for Zero-Shot Audio-Language Classificationcited by 0