Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings
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 datasets spanning noise, speech, monophonic musical notes, and music mixtures. Our primary finding is that all of these attributes are reliably recover...
| Authors | Héctor Martel, Joe Hennessy-Priest, Taemin Cho |
|---|---|
| Primary category | eess.AS |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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