Neural Network Systems and Methods for Removing Noise from Signals

Data signals, which come with a host of information, are often muddied with noise, impeding their effective interpretation and utility by processing algorithms. This noise can arise due to many factors such as electrical interference, faulty hardware or signal degradation. The need to clean these data signals precisely and quickly has risen, given the importance of rapid and accurate data processing in various sectors like telecommunication, healthcare, and science. The predominant approach to handle this issue entails engineering formal algorithms or filters to clean the noise. However, these traditional methods often fall short as they may not account for unpredicted noise patterns or sudden signal changes. Additionally, their inability to evolve or improve over time based on new noise patterns is a significant shortcoming, thereby necessitating a more dynamic, adaptive solution.

Technology Description

The invention, a noise-removal system, utilizes neural networks to selectively filter out noise from any given data signal from various sources. The eliminated noise is compared to pre-simulated noise patterns. This noise acts as a standard, representing expected noise disturbances that might be present in the data signal. This comparison aids in validating the efficiency of the noise clearance by the model. What differentiates this technology is its ability to continuously evolve and improve. It does not only remove noise statically but progressively learns using the comparison results against the simulated noise, and thereby trains the neural network accordingly. This self-improvement feature makes the system increasingly efficient at identifying and eliminating noise over time, enhancing the accuracy of the signal interpretation.