arXiv Machine Learning

Representation Matters in Randomized Smoothing for Audio Classification

arXiv:2606. 04210v1 Announce Type: cross Abstract: Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added.

arXiv AI

Jun 11

Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification

arXiv:2606. 11922v1 Announce Type: cross Abstract: Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST).

By Hemansh Shridhar, Miika Toikkanen, June-Woo Kim

llms benchmarks

arXiv Machine Learning

Jun 29

Advancing Speaker-Based Vocal Effort Classification with WavLM and Data Augmentation in Naturalistic Non-Calibrated Speech Recordings

arXiv:2606. 27543v1 Announce Type: cross Abstract: The variations in vocal effort range (e.

By Zahra Omidi, John H. L. Hansen

multimodal benchmarks

arXiv AI

Jun 2

VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio

arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.

By Maris Basha, Anja Zai, Sabine Stoll, Richard Hahnloser

rag multimodal benchmarks safety

arXiv AI

Jun 29

An Interpretable, Controllable Time-Varying IIR Denoiser for On-Device Assistive Hearing

arXiv:2603. 02794v2 Announce Type: replace-cross Abstract: We present TVF (Time-Varying Filtering), an interpretable, low-latency speech enhancement model for real-time, on-device assistive hearing.

By Riccardo Rota, Kiril Ratmanski, Jozef Coldenhoff, Milos Cernak

arXiv AI

Jul 7

Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings

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.

By H\'ector Martel, Joe Hennessy-Priest, Taemin Cho

rag multimodal

Hugging Face Trending Papers

Aug 3

Can Foundation Models Hear What Made That Sound? A Tiered Benchmark of Audio-Language Models and Traditional Classifiers for Closed-Set Sound Source Identification

We benchmark eleven audio classification methods: five task-aware closed-set LLMs (four Gemini models plus open-weight Kimi-Audio-7B-Instruct), four fixed-vocabulary taggers (YAMNet, PANNs, Whisper-AT, and SSLAM), a zero-shot audio-text model (CLAP), and an audio-grounded LLM (BAT). We evaluate them on a closed-set sound-source identification task over 2,242 clips spanning 23 fine-grained classes and 11 categories.

llms multimodal benchmarks

arXiv AI

Jul 1

BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations

arXiv:2606. 30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks.

By Ludovic K. Tuncay (IRIT-SAMoVA), Etienne Labb\'e (IRIT-SAMoVA), Thomas Pellegrini (IRIT-SAMoVA)

llms benchmarks

arXiv AI

Jun 11

Quality Adaptive Angular Margin Learning for Respiratory Sound Classification

arXiv:2606. 11915v1 Announce Type: cross Abstract: We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability.

By Yoon Tae Kim, Heejoon Koo, Miika Toikkanen, June-Woo Kim

benchmarks

arXiv Machine Learning

Jun 15

Beyond task performance: Decoding bioacoustic embeddings with speech features

arXiv:2606. 14662v1 Announce Type: new Abstract: Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task.

By Ines Nolasco, Jules Cauzinille, Marius Miron, Gagan Narula, Milad Alizadeh, Emmanuel Fernandez, Matthieu Geist, Ellen Gilsenan-McMahon, Olivier Pietquin, Emmanuel Chemla, Sara Keen

rag

arXiv Machine Learning

1d ago

Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift

arXiv:2608. 15037v1 Announce Type: cross Abstract: Audio-Text Foundation Models (ATMs) fail catastrophically under severe acoustic noise, yet existing adaptation strategies either rely on gradient-based Test-Time Adaptation (TTA), which reinforces noise rather than signal, or on prompt tuning that requires privileged noise annotations unavailable at inference.

By Ashish Anand Shukla, Rini Smita Thakur, Aryan Das, Vinod K. Kurmi

diffusion multimodal safety

arXiv Machine Learning

Jun 16

From Physics to Representation: Audio Learning with Synthetic Pre-training via Procedural Generation

arXiv:2606. 14791v1 Announce Type: cross Abstract: Self-supervised learning advances audio representation for multimedia analysis.

By Fengrui Liu, Ruiyang Huang, Qijian Zheng, Yuanfang Wang, Feng Liu

llms diffusion benchmarks safety

arXiv Machine Learning

1d ago

A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

arXiv:2608. 14824v1 Announce Type: cross Abstract: We present a parameter-free episodic evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings, across the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets.

By Christiaan M. Geldenhuys, Thomas R. Niesler

rag

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