scholarly journals Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

Author(s):  
Boris Karanov ◽  
Gabriele Liga ◽  
Vahid Aref ◽  
Domanic Lavery ◽  
Polina Bayvel ◽  
...  
2019 ◽  
Vol 3 (5) ◽  
Author(s):  
Yili Shen

This paper describes a branch of pattern recognition and lies in the field of digital signal processing. It is a speech recognition system of identifying different people speaking based on deep learning. In brief, this method can be used as intelligent voice control like Siri.


2019 ◽  
pp. 34-39 ◽  
Author(s):  
E.I. Chernov ◽  
N.E. Sobolev ◽  
A.A. Bondarchuk ◽  
L.E. Aristarhova

The concept of hidden correlation of noise signals is introduced. The existence of a hidden correlation between narrowband noise signals isolated simultaneously from broadband band-limited noise is theoretically proved. A method for estimating the latent correlation of narrowband noise signals has been developed and experimentally investigated. As a result of the experiment, where a time frag ent of band-limited noise, the basis of which is shot noise, is used as the studied signal, it is established: when applying the Pearson criterion, there is practically no correlation between the signal at the Central frequency and the sum of signals at mirror frequencies; when applying the proposed method for the analysis of the same signals, a strong hidden correlation is found. The proposed method is useful for researchers, engineers and metrologists engaged in digital signal processing, as well as developers of measuring instruments using a new technology for isolating a useful signal from noise – the method of mirror noise images.


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