GitHub

Making Acoustic Side-Channel Attacks on Noisy Keyboards Viable with LLM-Assisted Spectrograms "Typo" Correction

This repo is the implementation of a research project aimed at enhancing Acoustic Side-Channel Attacks (ASCAs) using a novel combination of Vision Transformers (VTs) and Large Language Models (LLMs).
IEEE

Real-Time Audio Noise Reduction and Speech Enhancement Using LadderNet With Hybrid Spectrogram Time-Domain Audio Separation Network

Abstract: In recent years, the rapid growth of modern technology has improved people lives, but it also comes with a downside: increased exposure to noise from complex industrial infrastructure and ...
Frontiers

Integration of PCG spectrogram texture and deep features for the diagnosis of heart failure with preserved ejection fraction using heterogeneous stacking ensemble learning

This study proposes a novel heterogeneous stacking ensemble learning model for the fusion of phonocardiogram (PCG) spectrogram texture and deep features to detect heart failure with preserved ejection ...
IEEE

DMEL: The Differentiable Log-Mel Spectrogram as a Trainable Layer in Neural Networks

Abstract: In this paper we present the differentiable log-Mel spectrogram (DMEL) for audio classification. DMEL uses a Gaussian window, with a window length that can be jointly optimized with the ...