This conceptual framework illustrates an optimal filtering workflow, starting with raw input signals and progressing through pre-processing, transformations, objective definition, and optimisation towards performance evaluation. On the left, various methods of preparing and transforming signals are shown, while the centre section focuses on formulating the optimisation problem and selecting suitable optimisation algorithms. Performance metrics and approaches for evaluating filter quality are presented on the right. Below, a HyperParameter (HP) sensitivity verification strategy ensures robust and verified filter designs. Abbreviations: AR: Auto-Regressive, CG: Congjugate Gradient, HT: Hilbert Transform, ICS2: Indicator of second-order CycloStationarity, QN: Quasi-Newton, RMSE: Root-Mean-Squared Error, SES: Squared Envelope Spectrum, SN: Spectral Negentropy, SNR: Signal-toNoise Ratio, SQP: Sequential Quadratic Programming, STFT: Short-Time Fourier Transform, WT: Wavelet Transform.