Suppose I have the following signal (Spectrogram):
I have three types of elements that I want to identify within this signal:
1) Noise 2) Call-1 3) Call-2
I have prior training of what each of the elements looks like and can obtain training for the duration of each call, and, the frequency ranges that each call will be present at. The task is to make a classifier for each of the training samples, and then iterate through the signal and detect the probability of each classifier.
The question is: At what data do I need for the classifier? For example, can I take training data and perform cross-correlation on the signal to detect which is the most likely classifier at the particular point in the signal.. Or look at the frequencies that are
known through training and detect whether it is noise, or, an actual signal?