Which kind of Deep Learning architecture (CNN, LSTM) could I use for classification duty of monodimension signal?

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Hello, I am trying to classify monodimensional signals (spectrum information) using Deep Learning algorithm. Having a dataset of 12000 observation, of 1x2048 samples (frequency taps), I tried to use CNN (NN toolbox of Matlab), with different convolution layer, without good result. I even tried to use LSTM but nothing change. Any suggestion?
Thanks in advance.

Accepted Answer

Vishal Bhutani
Vishal Bhutani on 10 Sep 2018
By my understanding, you want to train a Neural Network to classify one-dimensional signals. One of the thing you can try is Deep Neural Network with multiple hidden layers, there are various hyperparameter which you can vary: learning rate, number of neurons, number of hidden layers and if you are using recent MATLAB version you can vary the optimizer also same for LSTM. For CNN, try varying the size of filters, number of filters and learning rate. For 1-D data, mostly DNN or LSTM work, but you can try various networks. If possible try increasing dataset.
Alessio Izzo
Alessio Izzo on 12 Sep 2018
What about the validation of the LSTM? I saw it is not possibile to have the 'ValidationData' into the trainingOption. An alternative is to use CheckPoints and OutputFcn to (once per epoch) load the network from a checkpoint and run it against my validation data. But I did not manage to make it working. Any idea?

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