Publications (7)
Full-Capacity Unitary Recurrent Neural Networks
Scott Wisdom, Thomas Powers, John R. Hershey +2
Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural…
Using a Novel COVID-19 Calculator to Measure Positive U.S. Socio-Economic Impact of a COVID-19 Pre-Screening Solution (AI/ML)
Richard Swartzbaugh, Amil Khanzada, Praveen Govindan +8
The COVID-19 pandemic has been a scourge upon humanity, claiming the lives of more than 5.1 million people worldwide; the global economy contracted by 3.5% in 2020. This paper pres…
Complex Clipping for Improved Generalization in Machine Learning
Les Atlas, Nicholas Rasmussen, Felix Schwock +1
For many machine learning applications, a common input representation is a spectrogram. The underlying representation for a spectrogram is a short time Fourier transform (STFT) whi…
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
Scott Wisdom, Thomas Powers, James Pitton +1
In this paper, we propose a novel recurrent neural network architecture for speech separation. This architecture is constructed by unfolding the iterations of a sequential iterativ…
Enhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence
Scott Wisdom, Thomas Powers, Les Atlas +1
Most speech enhancement algorithms make use of the short-time Fourier transform (STFT), which is a simple and flexible time-frequency decomposition that estimates the short-time sp…
Estimating and Analyzing Neural Information Flow Using Signal Processing on Graphs
Felix Schwock, Julien Bloch, Les Atlas +2
Correlating neural communication in brain networks with behavior and cognition can provide fundamental insights into the functionality of both healthy and diseased brains. We demon…