# 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? – Engineer Feb 13 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. – Roshan Mehta Feb 13 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 – Engineer Feb 13 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. – Roshan Mehta Feb 13 at 16:10
• Do you have a lot of labeled data? – Engineer Feb 13 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.