Deep Learning for Acoustic Emission concrete fracture speciments: regression on-set time and classification of type of failure

Question

How can I use deep learning for both regression and classification tasks?

I am facing a problem with acoustic emission on fracture with concrete speciment. The objective is to find automatically the on-set time instant (time at the beginning of the acoustic emission) and the slope with the peak value to determine the kind of fracture (mode I or mode II based on the raise angle RA).

I have tried Regional CNN to work with images of the signals Fine-tuning Faster-RCNN using pytorch , but unfortunately the results are not outstanding up to now.

I would like to work with sequences (time series) of amplitude data according to a certain sampling frequency, but they have different length each. How can I deal with this problem?

Can I make a 1D-CNN which makes a sort of anomaly detection based on the supervised point that I can mark manually on training examples?

I have a certain number of recordings which I would like to exploit to train the model sampled at 100Hz. In examples on anomaly detection like Timeseries anomaly detection using an Autoencoder , they use the same time series and they perform a window with sliding 1 time step in order to obtain about 3700 to train their neural network. Instead I have different number of recordings (time series) each of them with a certain on-set time instant and different global length in seconds. How can I manage it?

I actually need the time instant of the beginning of the signal and the maximum point to define the raise angle and classify the type of fracture. Can I make classification directly with CNN simultaneously with regression tasks of the on-set time instant?

Thank you in advance!

Answer 1

I finally solved, thanks to the fundamental suggestion by @JonNordby, using Sound Event Detection method. We adopted and readapted the code from GitHub YashNita .

I labelled the data according to the following image:

Then, I adopted the method for extracting features from computing the spectrogram of the input signals:

And finally we were able to get a more precise output recognition of the Seismic Event Detection which is directly connected to the Acoustic Emission Event detection, obtaining the following result:

For the moment, only the event recognition phase was done, but it would be simple to readapt also to conduct classification of mode I or mode II of cracking.