US 12,136,197 B2
Neural network systems and methods for removing noise from signals
Michael Newey, Chelmsford, MA (US); and Prafull Sharma, Cambridge, MA (US)
Assigned to MASSACHUSETTS INSTITUTE OF TECHNOLOGY, Cambridge, MA (US)
Filed by Massachusetts Institute of Technology, Cambridge, MA (US)
Filed on Nov. 5, 2021, as Appl. No. 17/519,929.
Claims priority of provisional application 63/109,988, filed on Nov. 5, 2020.
Prior Publication US 2022/0138911 A1, May 5, 2022
Int. Cl. G06T 5/70 (2024.01); G01S 17/89 (2020.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06T 5/50 (2006.01)
CPC G06T 5/70 (2024.01) [G01S 17/89 (2013.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 5/50 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20224 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A method for removing noise from a data signal, the method comprising:
(a) receiving a first data signal that contains noise;
(b) removing, by a neural network, the noise from the first data signal to produce a processed data signal that represents the first data signal without the noise;
(c) calculating, from the first data signal and the processed data signal, a first noise signal representing the noise from the first data signal;
(d) generating a simulated noise signal;
(e) generating a comparison value by comparing the first noise signal and the simulated noise signal, the comparison value representing a correspondence between characteristics of the first noise signal and the simulated noise signal; and
(f) training the neural network using the comparison value to improve performance of the neural network in producing the processed data signal.