# Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings

arXiv: https://arxiv.org/abs/2607.03806 · HTML: https://mlsanity.com/paper/2607.03806/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 4 Jul 2026.

## Builds on (most cited first, 29 of 29)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [Adapting Frechet Audio Distance for Generative Music Evaluation](https://mlsanity.com/paper/2311.01616/) (2023)
- [Natural Language Supervision for General-Purpose Audio Representations](https://mlsanity.com/paper/2309.05767/) (2023)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [PAM: Prompting Audio-Language Models for Audio Quality Assessment](https://mlsanity.com/paper/2402.00282/) (2024)
- [Probing the Information Encoded in X-vectors](https://mlsanity.com/paper/1909.06351/) (2019)
- [T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining](https://mlsanity.com/paper/2404.17806/) (2024)
- [AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models](https://mlsanity.com/paper/2506.05140/) (2025)
- [DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning](https://mlsanity.com/paper/2410.09472/) (2024)
- [Text2FX: Harnessing CLAP Embeddings for Text-Guided Audio Effects](https://mlsanity.com/paper/2409.18847/) (2024)
- [AND: Audio Network Dissection for Interpreting Deep Acoustic Models](https://mlsanity.com/paper/2406.16990/) (2024)
- [Fx-Encoder++: Extracting Instrument-Wise Audio Effects Representations from Mixtures](https://mlsanity.com/paper/2507.02273/) (2025)
- [SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering](https://mlsanity.com/paper/2508.03448/) (2025)
- [TokenSynth: A Token-based Neural Synthesizer for Instrument Cloning and Text-to-Instrument](https://mlsanity.com/paper/2502.08939/) (2025)
- [Transformation of audio embeddings into interpretable, concept-based representations](https://mlsanity.com/paper/2504.14076/) (2025)
- [Causal Tracing of Audio-Text Fusion in Large Audio Language Models](https://mlsanity.com/paper/2603.13768/) (2026)
- [Investigating the Sensitivity of Pre-trained Audio Embeddings to Common Effects](https://mlsanity.com/paper/2501.15900/) (2025)
- [Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions](https://mlsanity.com/paper/2509.14785/) (2025)
- [FlowSynth: Instrument Generation Through Distributional Flow Matching and Test-Time Search](https://mlsanity.com/paper/2510.21667/) (2025)
- [Listen through the Sound: Generative Speech Restoration Leveraging Acoustic Context Representation](https://mlsanity.com/paper/2508.08953/) (2025)
- [Do Joint Language-Audio Embeddings Encode Perceptual Timbre Semantics?](https://mlsanity.com/paper/2510.14249/) (2025)

## Built on by (most cited first, 1 of 1)

- [Discriminative Axis, Not Data Volume: What a Contrastive Corpus Teaches an Audio Embedding](https://mlsanity.com/paper/2608.01560/) (2026)

## Abstract (arXiv)

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 recoverable from the CLAP embedding space across the examined datasets. Within this global picture, two encoding regimes emerge: RT60, LUFS, and RP are approximately linearly encoded, while SC requires non-linear probes. Both regimes generalize across eight additional audio foundation models, with the notable exception that amplitude-invariant architectures discard loudness entirely by construction. The identified linear feature directions are geometrically consistent across datasets for RT60 and LUFS, while highly domain-specific for RP. Finally, we provide a qualitative demonstration of cross-modal consistency, showing that text embeddings of acoustic descriptors align geometrically with the identified RT60 feature direction.