The paper introduces WST-Graph, a topology-preserving wavelet scattering front-end designed for speech deepfake detection. It reconstructs wavelet scattering paths into a sparse modulation‑carrier grid that feeds an AASIST graph backend, employing modulation‑level normalization and adaptive local attention pooling to produce fixed relative‑time representations while keeping acoustic axes intact. The resulting waveform‑to‑graph interface uses about 60% fewer trainable parameters than AASIST yet remains competitive, showing clear improvements on selected out‑of‑domain benchmarks.
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).
arXiv:2607. 20857v1 Announce Type: cross Abstract: Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse problems, but their training typically requires large volumes of simulated data.
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.
arXiv:2511. 21325v2 Announce Type: replace-cross Abstract: Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs.
Conditional Flow Matching models for text‑to‑speech often produce incoherent frequency evolution during inference. The authors propose a training‑free, frequency‑selective boosting strategy that uses the Discrete Wavelet Transform to dynamically modulate mel‑spectrogram sub‑bands during ODE integration, penalizing aggressive low‑frequency growth while boosting lagging high‑frequency details. Across multiple architectures, this method reduces the number of function evaluations from 32 to 26 and improves Frechet Audio Distance by up to 61% without harming mean opinion scores, speaker similarity, or intelligibility.
arXiv:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
arXiv:2607. 12584v1 Announce Type: cross Abstract: The rapid advancement of synthetic speech generation methods has made audio deepfake detection a critical challenge in multimedia forensics.
arXiv:2606. 00081v1 Announce Type: cross Abstract: Distributed Acoustic Sensing (DAS) enables large-scale monitoring through optical fibers, but its high dimensionality and complex spatio-temporal patterns make event classification demanding.
arXiv:2609.18009v1 Announce Type: cross Abstract: Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignment accura...
The rapid advancement of synthetic speech generation methods has made audio deepfake detection a critical challenge in multimedia forensics. While recent approaches achieve high detection accuracy, they typically rely on black-box architectures that offer limited interpretability and high computational complexity.
arXiv:2609.18009v2 Announce Type: replace-cross Abstract: Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignmen...
arXiv:2607. 17761v1 Announce Type: cross Abstract: Recently, speech deepfake detection (SDD) has achieved significant progress.