IEEE

Model-Guided Deep Learning for Line Segment Detection in Time–Frequency Spectrograms of an Ocean Waveguide

Abstract: The application of machine learning in underwater acoustics is often limited by the lack of high-quality data. One method to avoid this data issue is to use modeled data to train a machine ...
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 ...
Microsoft

High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training

The development of machine learning for cardiac care is severely hampered by privacy restrictions on sharing real patient electrocardiogram (ECG) data. Although generative AI offers a promising ...
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 ...