MADS (Multi-view Acoustic Descriptor Set) is a compact 19‑dimensional, physics‑informed descriptor set designed to capture spectral, temporal, mechanical, and stochastic aspects of audio signals. Unlike traditional log‑mel or MFCC representations, MADS encodes excitation, damping, periodicity, impulsiveness, and structural consistency in a unified multi‑view format. Evaluated on ESC‑10, ESC‑50, and MSoS datasets with classical machine learning models, MADS outperforms conventional 26‑D MFCC and 38‑D spectral‑summary baselines, achieving 81.00% on ESC‑10, 52.78% on ESC‑50, and 67.48% on MSoS while using roughly half the dimensionality of the 38‑D baseline.
By Utsab Ghosh, Roshni Chakraborty
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
arXiv:2511. 21325v2 Announce Type: replace-cross Abstract: Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs.
By Ido Nitzan Hidekel, Gal lifshitz, Khen Cohen, Dan Raviv
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
arXiv:2608. 13817v1 Announce Type: cross Abstract: Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals.
By Tom\'as Andrade Weber
AudioFuse is a hybrid architecture that jointly learns from spectrograms and raw waveforms to classify phonocardiograms. It combines a wide-and-shallow Vision Transformer for spectral features with a shallow 1D CNN for temporal waveforms, reducing overfitting while capturing complementary information. On the PhysioNet 2016 dataset, AudioFuse achieves a state‑of‑the‑art ROC‑AUC of 0.8608 and shows superior robustness to domain shift on the PASCAL dataset, outperforming both spectrogram‑only and waveform‑only baselines.
By Md. Saiful Bari Siddiqui, Utsab Saha
arXiv:2607. 08545v1 Announce Type: cross Abstract: End-to-end neural audio models achieve high-fidelity compression and generation.
By Nicole Cosme-Clifford
arXiv:2606. 04210v1 Announce Type: cross Abstract: Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added.
By Jong-Ik Park, Shreyas Chaudhari, Jos\'e M. F. Moura, Carlee Joe-Wong
arXiv:2607. 05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech.
By Zhifeng Kong, Sang-gil Lee, Jaehyeon Kim, Boxin Wang, Zihan Liu, Sungwon Kim, Yang Chen, Arushi Goel, Rajarshi Roy, Wenliang Dai, Zhuolin Yang, Yangyi Chen, Dongfu Jiang, Sreyan Ghosh, Tuomas Rintamaki, Andrew Tao, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping
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
Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text LLM built on Nemotron-Cascade-2-30B-A3B, a strong text-only MoE LLM.
arXiv:2607. 00247v1 Announce Type: cross Abstract: Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors.
By Aaron Isidore Grace, Zhouyuan Huo, Weiran Wang