AI-Driven Signal Processing: Improving Communication Systems with Machine Learning-Based Noise Reduction

Signal processing plays an important role for transforming and analysing real-world signals like audio Important to process the signal efficiently and precisely. An AI approach to noise reduction using machine learning techniques are investigated in this work. We then use modern techniques such as Fourier Transform, Principal Component Analysis (PCA) and de-noising autoencoders in order to remove noise while retaining important signal features. Method One proposed to employ machine learning models to adaptively eliminate the noise according to communication system, thus to improve the performance of communication systems. The experimental results suggest that using the artificial intelligence models to suppress the noise can suppress the noising efficiency and restore the signal nearly completely. These results illustrate the tremendous potential of AI as applied to signal processing in background noise. The AI models have also proved that, because they are trained to adapt to the kind of noise in the moment that it happens, they deliver a much better-quality signal than traditional methods. These characteristics are essential for the future communication systems which are required very high robustness and clearness.