Binary classification with acousitc attributes


I am currently working on a college project where the goal is to classify audio recordings as depressed or not. I have got 74 acoustic attributes assigned continuously for each recording where the recording is segmented every 10ms. So for every recording I have several thousands data points with 74 acoustic attributes. In total I have 106 recordings and output labels for each of them.

I found Bayesian approach appropriate for this project as uncertainty in the prediction is crucial. It is also as excuse to learn about this field as I am really new to it.

I can’t seem to understand how should I build the model. I know I have to use bernoulli dist to represent the output and probably a beta dist for the p value as prior. But where should I include the input datasets.

How should I approach this one?

You have to use the observed argument to input the observed data into the model. But from the questions you asked, I think you need to get familiar with PyMC3 and Bayesian modeling in general. I put together a list of useful educational ressources in another thread – I’m sure they will help you :vulcan_salute: