This technology addresses the challenge of efficiently training AI models on complex signals and images by introducing an advanced preprocessing framework. This framework transforms raw data into an optimized, multi-scale representation, which reduces noise and redundancy while highlighting relevant features. Consequently, machine learning models can focus directly on learning patterns, resulting in faster, more computationally efficient, and potentially more accurate training. By improving input data quality, the approach supports more sustainable AI pipelines, lowering computational costs and energy use. Its general and modular design allows easy integration into existing AI workflows across various sectors.