An Intelligent Signal Processing Method Based on Random Forest and CNN

In fields such as digital communication, audio processing, and image processing, the techniques for encoding and decoding signals and performing noise reduction play a pivotal role in ensuring the quality of information transmission. This paper tends to harness the capabilities of machine learning and neural networks to devise intelligent solutions for signal encoding and decoding with the objectives of enhancing signal quality, alleviating congestion, and optimizing communication systems. Firstly, the experiment focuses on using random forests and Convolutional Neural Networks (CNN) to identify signal encoding methods, followed by the implementation of efficient noise reduction techniques. This paper findings demonstrate that this integrated approach attains an accuracy level of approximately 92% in recognizing various signal encoding methods. Furthermore, it has been observed that when subjected to interference, post-processing leads to significant improvements in the Root Mean Square Error (RMSE) associated with each encoding method, thus ensuring high-quality noise reduction.