arXiv:2607.03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood. In this work, we analyze CLAP audio embeddings through a probing framework, studying the encoding of three fundamental perceptual dimensions: reverberation (RT60), loudness (LUFS), and spectral content, measured via spectral centroid (SC) and relative pitch (RP). Probes of increasing complexity are trained to predict each attribute from frozen embeddings across five data
The rapid adoption of audio foundation models makes understanding their internal workings a priority for continued development and reliable application.
Insights into how AI models encode fundamental acoustic attributes directly informs the development of more robust, interpretable, and controllable AI systems, impacting fields from speech recognition to synthetic audio.
This research provides a more granular understanding of CLAP audio embeddings, enabling better model design and troubleshooting by clarifying what specific features are learned and how.
- · AI researchers
- · Audio AI developers
- · Companies building on audio foundation models
- · Developers relying on black-box audio AI models
- · Companies with less sophisticated audio AI understanding
Improved understanding of audio foundation models leads to more targeted development of AI for sound processing.
Enhanced interpretability and control over audio AI facilitates new applications in areas requiring high precision, such as medical diagnostics or industrial monitoring.
A clearer picture of AI's internal representations could accelerate generalized AI development by informing how AI models perceive and interact with specific sensory data.
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Read at arXiv cs.AI