EarLog is a real‑time, embedded system that opportunistically uses headphone playback as a probe to detect the acoustic reflex (AR), an involuntary ear response indicating protective activation during high‑load listening. By employing content‑conditioned residual sensing, a resonance‑constrained sparse FIR model, and dual‑microphone observations, EarLog suppresses noise from nonstationary content, individual ear acoustics, and device variations to identify weak AR signals. In a study with 81 participants, 16 headphone models, and over 400 hours of recordings, EarLog achieved 99.0% sensitivity and 98.7% specificity against a clinically referenced benchmark, enabling continuous, listener‑specific AR monitoring during everyday listening.
By Yongzhi Huang, Jiayi Zhao, Kaishun Wu
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
The paper introduces a simulation-based method for detecting a user's own voice in hearing aids using only a single microphone. It employs a data augmentation strategy with simulated acoustic transfer functions to train a transformer classifier, achieving over 90% accuracy on both simulated and real-world recordings. The approach reduces hardware complexity and power consumption while maintaining robust performance across varied spatial conditions.
By Mathuranathan Mayuravaani, W. Bastiaan Kleijn, Andrew Lensen, Charlotte S{\o}rensen
arXiv:2603. 00395v3 Announce Type: replace-cross Abstract: Hearables are becoming ubiquitous, yet their sound controls remain blunt: users can either enable global noise suppression or focus on a single target sound.
By Seunghyun Oh, Malek Itani, Aseem Gauri, Shyamnath Gollakota
arXiv:2602. 01394v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noise.
By Yochai Yemini, Yoav Ellinson, Rami Ben-Ari, Sharon Gannot, Ethan Fetaya
VM-ARRAYDPS introduces virtual microphones with higher signal‑to‑noise ratios to augment a limited physical microphone array, providing additional multi‑channel consistency constraints for blind speech separation. By leveraging a pretrained speech diffusion model and posterior sampling, the method improves upon the existing ArrayDPS approach. Experiments on 2‑speaker and 3‑speaker datasets show significant performance gains, and ablation studies reveal the impact of virtual microphone count and consistency weighting.
By Jingqi Sun, Haozhan Tang, Shulin He, Zhong-Qiu Wang