arXiv:2511. 21325v2 Announce Type: replace-cross Abstract: Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs.
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arXiv:2606. 02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment.
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.
Dominant audio classification pipelines rely either on compact handcrafted summaries or on fixed time-frequency frontends such as log-mel representations prior to deep modeling. While highly successfu...
arXiv:2607. 08545v1 Announce Type: cross Abstract: End-to-end neural audio models achieve high-fidelity compression and generation.
arXiv:2607. 17761v1 Announce Type: cross Abstract: Recently, speech deepfake detection (SDD) has achieved significant progress.
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.