Audio-to-Image Bird Species Retrieval without Audio-Image Pairs via Text Distillation

• URL: http://arxiv.org/abs/2602.00681v1
• Date: Sat, 31 Jan 2026 11:55:54 GMT
• Title: Audio-to-Image Bird Species Retrieval without Audio-Image Pairs via Text Distillation
• Authors: Ilyass Moummad, Marius Miron, Lukas Rauch, David Robinson, Alexis Joly, Olivier Pietquin, Emmanuel Chemla, Matthieu Geist,
• Abstract summary: We propose a simple and data-efficient approach that enables audio-to-image retrieval without audio-image supervision.<n>We distill the text embedding space of a pretrained image-text model into a pretrained audio-text model (BioLingual) by fine-tuning its audio encoder with a contrastive objective.<n>We evaluate the resulting model on multiple bioacoustic benchmarks.
• Score: 34.70927931880309
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio-to-image retrieval offers an interpretable alternative to audio-only classification for bioacoustic species recognition, but learning aligned audio-image representations is challenging due to the scarcity of paired audio-image data. We propose a simple and data-efficient approach that enables audio-to-image retrieval without any audio-image supervision. Our proposed method uses text as a semantic intermediary: we distill the text embedding space of a pretrained image-text model (BioCLIP-2), which encodes rich visual and taxonomic structure, into a pretrained audio-text model (BioLingual) by fine-tuning its audio encoder with a contrastive objective. This distillation transfers visually grounded semantics into the audio representation, inducing emergent alignment between audio and image embeddings without using images during training. We evaluate the resulting model on multiple bioacoustic benchmarks. The distilled audio encoder preserves audio discriminative power while substantially improving audio-text alignment on focal recordings and soundscape datasets. Most importantly, on the SSW60 benchmark, the proposed approach achieves strong audio-to-image retrieval performance exceeding baselines based on zero-shot model combinations or learned mappings between text embeddings, despite not training on paired audio-image data. These results demonstrate that indirect semantic transfer through text is sufficient to induce meaningful audio-image alignment, providing a practical solution for visually grounded species recognition in data-scarce bioacoustic settings.

Related papers

• SeeingSounds: Learning Audio-to-Visual Alignment via Text [15.011814561603964]
  We introduce SeeingSounds, a framework for audio-to-image generation that leverages the interplay between audio, language, and vision.<n>Our method performs dual alignment: audio is projected into a semantic language space via a frozen language encoder, and, contextually grounded into the visual domain using a vision-language model.<n>This approach, inspired by cognitive neuroscience, reflects the natural cross-modal associations observed in human perception.
  arXiv  Detail & Related papers  (2025-10-10T18:42:50Z)
• Zero-Shot Audio Captioning via Audibility Guidance [57.70351255180495]
  We propose three desiderata for captioning audio -- (i) fluency of the generated text, (ii) faithfulness of the generated text to the input audio, and (iii) audibility. Our method is a zero-shot method, i.e., we do not learn to perform captioning. We present our results on the AudioCap dataset, demonstrating that audibility guidance significantly enhances performance compared to the baseline.
  arXiv  Detail & Related papers  (2023-09-07T17:45:58Z)
• AdVerb: Visually Guided Audio Dereverberation [49.958724234969445]
  We present AdVerb, a novel audio-visual dereverberation framework. It uses visual cues in addition to the reverberant sound to estimate clean audio.
  arXiv  Detail & Related papers  (2023-08-23T18:20:59Z)
• Align, Adapt and Inject: Sound-guided Unified Image Generation [50.34667929051005]
  We propose a unified framework 'Align, Adapt, and Inject' (AAI) for sound-guided image generation, editing, and stylization. Our method adapts input sound into a sound token, like an ordinary word, which can plug and play with existing Text-to-Image (T2I) models. Our proposed AAI outperforms other text and sound-guided state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-20T12:50:49Z)
• CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models [50.42886595228255]
  We propose to learn the desired text-audio correspondence by leveraging the visual modality as a bridge. We train a conditional diffusion model to generate the audio track of a video, given a video frame encoded by a pretrained contrastive language-image pretraining model.
  arXiv  Detail & Related papers  (2023-06-16T05:42:01Z)
• CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos [44.14061539284888]
  We propose to approach text-queried universal sound separation by using only unlabeled data. The proposed CLIPSep model first encodes the input query into a query vector using the contrastive language-image pretraining (CLIP) model. While the model is trained on image-audio pairs extracted from unlabeled videos, at test time we can instead query the model with text inputs in a zero-shot setting.
  arXiv  Detail & Related papers  (2022-12-14T07:21:45Z)
• AudioGen: Textually Guided Audio Generation [116.57006301417306]
  We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive model that generates audio samples conditioned on text inputs.
  arXiv  Detail & Related papers  (2022-09-30T10:17:05Z)
• Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis [67.73554826428762]
  We propose a novel audio-visual speech enhancement framework for high-fidelity telecommunications in AR/VR. Our approach leverages audio-visual speech cues to generate the codes of a neural speech, enabling efficient synthesis of clean, realistic speech from noisy signals.
  arXiv  Detail & Related papers  (2022-03-31T17:57:10Z)
• Sound-Guided Semantic Image Manipulation [19.01823634838526]
  We propose a framework that directly encodes sound into the multi-modal (image-text) embedding space and manipulates an image from the space. Our method can mix different modalities, i.e., text and audio, which enrich the variety of the image modification. The experiments on zero-shot audio classification and semantic-level image classification show that our proposed model outperforms other text and sound-guided state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-30T13:30:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.