Classifying 2 Classes of Ultrasound Signal Using Machine Learning by Frequency Domain

Question

I have two samples which, when exposed to ultrasound, emit their unique frequency responses. As can be seen in the attached figure, where the exciting frequency is 2.25 MHz, sample 1 emits a strong subharmonic and also ultra-harmonics which is not the case for sample 2.

My 1st question is, if I want to classify samples 1 and 2 using machine learning, how can I make use of the subharmonic and ultra-harmonics? The goal is that, when I want to know whether a signal is from sample 1 or sample 2, the ML algorithm would be able to tell that based on the presence or absence of the sub and ultra harmonics. For sample 2, the harmonics present are lower in power but I don't want to rely on amplitude.

My 2nd question is, is there any way to separate sample 1 and 2 signals from a signal coming from their mixture using machine learning?

Thanks in advance.

Answer 1

One simple way is to create a feature set based on the energy on the different frequency bins.

If the case above is representative, even "small guns" (Linear classifiers) will solve this.

Regarding the mixing, it depends if the mix is a sum. If it is, then it will be able to classify it if there is a difference in energy ratio between the different bins.