Depending on the application, a simple signal processing algorithm may be sufficient to extract the necessary information from the signal. Additional views of the data can be obtained with transformations such as Fourier and Wavelet. Filtering before transformations may be beneficial to address boundary effects.
If your application uses Artificial Intelligence and Machine Learning, it may still benefit from signal conditioning/processing and feature extraction. Carefully designed input features may reduce the amount of data needed to train a model or may simplify the model requirements. This may reduce data acquisition cost, time to train a model, and implementation costs. Signal processing and feature extraction can also assist in model interpretability.
With all the new AI and ML techniques, it might be argued that signal processing and feature extraction is becoming obsolete. For this to be true, generally much more data is required and collecting good data is expensive. Whether you go this route or use traditional signal processing methods, be sure that your investment is being well managed. (See Data Management.)
We can assess your signal processing needs, or you may already know the type of signal processing you would like to have done. We can perform a variety of signal processing methods to meet your needs.