← Back to all news
arXiv AI

Decay-Region Group Delay as a Forensic Cue for AI-Generated Impulsive Sounds

The study explores whether AI‑generated impulsive sounds can be distinguished from real sounds using group delay analysis. It finds that while onset‑region group‑delay distributions are nearly identical, the decay‑region shows a significant divergence (KL = 0.322 vs. 0.022). Various classifiers—single‑feature AUC ≈ 0.720, Random Forest AUC ≈ 0.884, and CNN/transformer models achieving up to 94% accuracy—demonstrate that decay‑region group delay provides strong discriminative power, with Random Forest remaining stable across different STFT settings.

Related stories

arXiv AI
Oct 27

Feature Scaling Beats Architecture Choice in Offline Respiratory Screening for Low-Resource Clinics

The study compares classical and deep learning classifiers for a five‑class respiratory‑sound dataset of 1,211 clips. An SVM using 30 summary features (MFCC, ZCR, RMS) achieves macro recall of 0.78, while a 1.13M‑parameter convolutional‑recurrent network reaches 0.75–0.77; no clear advantage is found between model families. The largest performance gain comes from standardizing the SVM’s features, which boosts macro recall by 27 points, far exceeding any architectural difference.

By Abubakar Chilala, Cleophas Kadima, Innocent Anyaele, Victor Olufemi
More like this →
arXiv Machine Learning
Jul 9

Generalist Vision-Language Models for Fast Radio Burst detection: a zero-shot benchmark against a specialized detector

arXiv:2607. 07382v1 Announce Type: new Abstract: Fast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning models.

By Raiff H. Santos, Amilcar R. Queiroz, Tharcisyo S. S. Duarte, K. E. L. de Farias, Rafael A. Batista
llmsfine-tuningmultimodalbenchmarks
More like this →
arXiv Machine Learning
Sep 3

Half-Truth Audio Detection and Localisation: A Lightweight Cross-Attentive Architecture and a Cross-Corpus Diagnostic Study

The paper introduces CAFNet, a lightweight cross‑attentive neural network that fuses MFCC, LFCC, and Chroma‑STFT features to detect and localise partially manipulated (half‑truth) speech. CAFNet achieves high ternary accuracy (97.55%) and low boundary mean absolute error (0.037 s) on the MLADDC benchmark, while demonstrating that cross‑corpus transfer depends on both capability and corpus characteristics. Ablation studies show that cross‑attention fusion is the most critical component, and removing a deeply supervised auxiliary head improves in‑domain performance and reduces variance.

By S. Sutharya, Remya K. Sasi
benchmarks
More like this →
arXiv AI
Sep 2

MADS: A Multiview Acoustic Descriptor Set Beyond Standard Spectral Summaries

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
More like this →
arXiv Machine Learning
Sep 24

"What's That Sound?": A Versatile, Robust, and Lightweight Convolutional Transformer for Environment Sound Recognition

The paper introduces RALCT, a lightweight Convolutional Transformer that combines randomized audio augmentations, MFCCs, and log‑mel spectrograms to extract robust features for environmental sound recognition. With only about 310,000 parameters, RALCT achieves state‑of‑the‑art accuracy—over 93% on UrbanSound8K, peaking at 94.56%—making it suitable for deployment on mobile devices. The authors also develop a mobile app that integrates the model to provide real‑time safety alerts for hearing‑impaired users.

By Julia Huang
llmsbenchmarkssafety
More like this →
arXiv AI
Sep 10

AudioFuse: Unified Spectral-Temporal Learning via a Hybrid ViT-1D CNN Architecture for Robust Phonocardiogram Classification

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
llmscomputer-visionbenchmarkssafety
More like this →