machine learning on multiple signal at once

I am very new signal processing and I am asking this with the expectation that someone gives me a concrete guideline on how to approach my problem.

The problem I need to solve is that I have 2 signals (shown in green and yellow) which look like this as below in original scales and log scale Based on the interaction (e.g. cutting each other) of these 2 signals, few features are calculated like i.e. range where both signals are low, the range where signals are up and only a threshold difference in amplitude value but now I need some kind of Machine learning may be RNN or LSTM or 1D CNN. I have a set of pre-calculated values and corresponding signals. Now I am wondering how to apply machine learning techniques while looking at both these signals together.

• The question isn't clear to me. You want to build a "machine learning" box, the input is the green or yellow signal and what is the output? What are the purple and pink lines in the plots? Feb 13 '20 at 14:53
• Just ignore purple and pink plots. input: 2 input signals, output: maybe another 1 signal with step 4 discrete values as 0,1,2,3. Feb 13 '20 at 15:16
• So you input the green and yellow signals and want to output "maybe another 1 signal"? I really don't understand what you're trying to do. What do the signals mean? Give some context so people understand what you're doing Feb 13 '20 at 15:28
• e.g input: time-based 2 sensors values say for 1 AM-2 AM hour from a user, output: the range of times e.g where the user was doing activity 1, activity 2, activity X on 1:10-1:15, 1:15-1:30, 1:30-2:00 with a confidence value. Feb 13 '20 at 16:10
• Do you have a lot of labeled data? Feb 13 '20 at 16:14

For example, say you input two signals ($$x_1[n], x_2[n]$$) of length 1024 and say you had activity #1 during samples 0-255, activity #2 from 256-511, and all other time is activity #0. Then you could format your output to be a signal of length 1024 (equal to length of input signals), to be: $$y[n]= \begin{cases} 1, & 0 \leq n \leq 255 \\ 2, & 256 \leq n \leq 511 \\ 0, & n \geq 512 \end{cases}$$. I bet this requires a bit of preprocessing of your dataset but this is not uncommon at all when doing these sorts of things.