Virtual Acoustic Channel Expansion Based on Neural Networks for Weighted Prediction Error-Based Speech Dereverberation

2020 ◽  
Author(s):  
Joon-Young Yang ◽  
Joon-Hyuk Chang
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Acoustic Channel ◽  
Speech Dereverberation ◽  
Channel Expansion

scholarly journals Asymptotic Prediction Error Variance for Feedforward Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 1108-1113
Author(s):  
Magnus Malmström ◽  
Isaac Skog ◽  
Daniel Axehill ◽  
Fredrik Gustafsson
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Feedforward Neural Networks ◽  
Error Variance ◽  
Prediction Error Variance

scholarly journals Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models

2003 ◽  
Vol 10 (6) ◽  
pp. 585-587 ◽  
Author(s):  
Th. D. Xenos ◽  
S. S. Kouris ◽  
A. Casimiro
Keyword(s):  
Neural Networks ◽  
Faraday Rotation ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Time Span ◽  
Time Dependent ◽  
Forecasting Models ◽  
Error Margin ◽  
The Difference ◽  
Tec Variability

Abstract. An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of ± 10% is considered, the population percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the foF2 - TEC variability difference and on hysteresis-like effect between these two ionospheric characteristics.

Adaptive minimum prediction‐error deconvolution and source wavelet estimation using Hopfield neural networks

Geophysics ◽  
1992 ◽  
Vol 57 (5) ◽  
pp. 670-679 ◽  
Author(s):  
Li‐Xin Wang ◽  
Jerry M. Mendel
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Seismic Signal ◽  
Estimation Problem ◽  
Hopfield Neural Networks ◽  
Stable States ◽  
Energy Functions ◽  
Wavelet Estimation ◽  
Artificial Neural ◽  
Seismic Signal Processing

The massively parallel processing advantage of artificial neural networks makes them suitable for hardware implementations; therefore, using artificial neural networks for seismic signal processing problems has the potential of greatly speeding up seismic signal processing. A commonly used artificial neural network—Hopfield neural network—is used to implement a new adaptive minimum prediction‐error deconvolution (AMPED) procedure which decomposes deconvolution and wavelet estimation into three subprocesses: reflectivity location detection, reflectivity magnitude estimation, and source wavelet extraction. A random reflectivity model is not required. The basic idea of the approach is to relate the cost functions of the deconvolution and wavelet estimation problem with the energy functions of these Hopfield neural networks so that when these neural networks reach their stable states, for which the energy functions are locally minimized, the outputs of the networks give the solution to the deconvolution and wavelet estimation problem. Three Hopfield neural networks are constructed to implement the three subprocesses, respectively, and they are then connected in an iterative way to implement the entire deconvolution and wavelet estimation procedure. This approach is applied to synthetic and real seismic traces, and the results show that: (1) the Hopfield neural networks converge to their stable states in only one to four iterations; hence, this approach gives a solution to the deconvolution and wavelet estimation problem very quickly; (2) this approach works impressively well in the cases of low signal‐to‐noise ratio and nonminimum phase wavelets; and (3) this approach can treat backscatter either as noise or as useful signal.

scholarly journals Are Markets Truly Efficient? Experiments using Deep Learning for Market Movement Prediction

2018 ◽  
Author(s):  
Sanjiv Das ◽  
Karthik Mokashi ◽  
Robbie Culkin
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Market Efficiency ◽  
Prediction Error ◽  
Weak Form ◽  
Movement Prediction ◽  
Index Decomposition ◽  
Information Set ◽  
Underlying Stocks ◽  
Future Direction

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

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