Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Atlas, Les"'
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) which gives complex values. The spectrogram uses the magnitude of these c
Externí odkaz:
http://arxiv.org/abs/2302.13527
Correlating neural communication in brain networks with behavior and cognition can provide fundamental insights into the functionality of both healthy and diseased brains. We demonstrate how communication in the brain can be estimated from recorded n
Externí odkaz:
http://arxiv.org/abs/2205.13719
Autor:
Swartzbaugh, Richard, Khanzada, Amil, Govindan, Praveen, Pilanci, Mert, Owoyemi, Ayomide, Atlas, Les, Estrada, Hugo, Nall, Richard, Lotito, Michael, Falcone, Rich, J, Jennifer Ranjani
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 presents a COVID-19 calculator, synthesizing existing published calculator
Externí odkaz:
http://arxiv.org/abs/2201.11109
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 iterative soft-thresholding algorithm (ISTA) that solves the optimization prob
Externí odkaz:
http://arxiv.org/abs/1709.07124
Recurrent neural networks (RNNs) are powerful and effective for processing sequential data. However, RNNs are usually considered "black box" models whose internal structure and learned parameters are not interpretable. In this paper, we propose an in
Externí odkaz:
http://arxiv.org/abs/1611.07252
Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural networks (uRNNs), which use unitary recurrence matrices, have recently
Externí odkaz:
http://arxiv.org/abs/1611.00035
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 spectrum of a signal. However, the duration of short STFT frames are inh
Externí odkaz:
http://arxiv.org/abs/1509.00533
Autor:
Larson, Eric, Froehlich, Jon, Campbell, Tim, Haggerty, Conor, Atlas, Les, Fogarty, James, Patel, Shwetak N.
Publikováno v:
In Pervasive and Mobile Computing 2012 8(1):82-102
Publikováno v:
SAE Transactions, 1997 Jan 01. 106, 389-395.
Externí odkaz:
https://www.jstor.org/stable/44650413
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