I have a N data samples where each sample is a signal of varying length. The important information relating to associated classlabel could be anywhere in that signal? As such, I don't want to chop of some of my signals to make all of them equal length. People suggested me to use recurrent neural network but I can't formalize my problem with RNN. In NLP (where I see most of RNNs being used) they want to predict a word looking at previous words but here my whole signal has one label not different labels.
Looking forward for some insightful answers.