Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization (2505.05343v1)

Abstract: Large-scale vision-LLMs demonstrate strong multimodal alignment and generalization across diverse tasks. Among them, CLIP stands out as one of the most successful approaches. In this work, we extend the application of CLIP to sound source localization, proposing a self-supervised method operates without explicit text input. We introduce a framework that maps audios into tokens compatible with CLIP's text encoder, producing audio-driven embeddings. These embeddings are used to generate sounding region masks, from which visual features are extracted and aligned with the audio embeddings through a contrastive audio-visual correspondence objective. Our findings show that alignment knowledge of pre-trained multimodal foundation model enables our method to generate more complete and compact localization for sounding objects. We further propose an LLM-guided extension that distills object-aware audio-visual scene understanding into the model during training to enhance alignment. Extensive experiments across five diverse tasks demonstrate that our method, in all variants, outperforms state-of-the-art approaches and achieves strong generalization in zero-shot settings.

• The paper proposes a self-supervised framework for sound source localization that adapts CLIP using an AudioTokenizer to process audio input without needing textual prompts.
• It converts audio to tokens via an AudioTokenizer for CLIP's text encoder, aligning modalities through contrastive and LLM-guided learning.
• Experimental validation shows the method achieves substantial improvements over state-of-the-art models and robust zero-shot generalization on various localization and segmentation tasks.

An Examination of "Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization"

The exploration of audio-visual learning has seen a significant advancement with the introduction of "Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization" by Sooyoung Park, Arda Senocak, and Joon Son Chung. This paper presents a sophisticated, self-supervised approach that extends the capacities of large-scale multimodal models such as CLIP to the domain of sound source localization, remarkably without the reliance on textual input. The methodology leverages the CLIP model's inherent robust multimodal alignment, adapting it innovatively for audio-visual tasks through ingenious use of an AudioTokenizer.

Framework and Methodology

The proposed framework bypasses the conventional requirement of textual prompts by transforming audio inputs into tokens interpretable by CLIP’s text encoder. This transformation results in the generation of audio-driven embeddings, which are then aligned with visual content via a contrastive learning objective, enhancing the framework's capacity to localize sound sources across diverse visual environments.

A critical component of the architecture is the use of pre-trained models: CLIP for the text and vision encoders, and an audio encoder pre-trained for audio representation. The AudioTokenizer is key, as it processes auditory data into discrete tokens, which, when combined with a static textual prefix, allows the CLIP text encoder to produce context-rich embeddings that maintain semantic coherence across modalities.

In addition to standard audio-visual grounding techniques, the authors introduce an LLM-guided training objective. This facet further augments the model's alignment capabilities by using generated captions from both audio and visual domains, processed via a LLM to extract object-specific context, thus refining the training process through contextually enriched self-supervision.

Experimental Validation

The validity and generalization of the proposed method are substantiated through comprehensive experimental evaluations on multiple datasets. The framework exhibits substantial improvements over state-of-the-art models across tasks that include single-source localization, segmentation, and even multi-source environments, often achieving robust zero-shot generalization capabilities. The model's performance, as demonstrated by superior metrics like cIoU, AUC, and mIoU, underscores its effective alignment of audio and visual modalities without necessitating additional label propagation or post-processing techniques.

In varied scenarios such as noisy environments or multi-source audio-visual intersections, the framework consistently outperforms competing models. This indicates a well-learned semantic correspondence, allowing the model to accurately distinguish and localize individual sound sources within complex scenes.

Implications and Future Prospects

The implications of this research are significant for multimodal AI applications. By eliminating the need for explicit text supervision while achieving remarkable alignment and localization precision, the framework is poised to influence future developments in sound recognition technologies embedded in multi-sensor environments. The potential applications extend to fields such as autonomous navigation, surveillance, and assistive technologies where accurate audio-visual signal processing is crucial.

Moreover, the integration of LLM guidance highlights an unexplored avenue of leveraging LLMs to augment visual understanding tasks. Future exploration might explore optimizing multimodal models using end-to-end frameworks that dynamically incorporate textual, audio, and visual inputs in real-time, enhancing contextual and environmental understanding.

In conclusion, the paper sets a new benchmark in the integration of pre-trained multimodal models for audio-visual tasks, offering both technical insights and practical methodologies for refining real-world AI applications. The advancements discussed reflect a promising direction in self-supervised learning, where machines not only see but effectively hear and interpret their environments.