So I’ve been working in my past time building an impulse and an application that interfaces with the API to handle the sending of data and the classification/results.
Alls well, I have an impulse that can detect the number of coughs in an X length sample with about 76% accuracy. But I realized something, my cough datasets lengths range from 1 second to 1 minute. So I’ll have a 1 second sample with two coughs, or one. I’ll have a 1 minute sample with about 40 coughs over that period or time, and different permutations of this, etc.
But my question is, if I wanted to increase the accuracy and the ability to detect the NUMBER of coughs, not just a coughing event, should I strip all of my data down into 500ms-1000ms samples of just single coughs? Or is it better/okay to have longer samples with noise in between coughs.
Also, in terms of my window parameters, I’ve done a lot of testing and the smallest of changes can COMPLETELY alter the classification results. Does anyone have any advice in terms of finding a good set of parameters for a task like this?
Right now, on average one cough usually fits into a window of about 400-500ms. So in order to not over count one cough, my increase is usually >50%, so around 350-400 for instance. Is this advisable? Is there something better I could be doing? Any tips or advice in terms of building a robust and accurate impulse?
Again, not attempting to detect coughing EVENTS but rather the number of coughs in a sample.