"No Classification" for a CNN Neural Network on Audio It it possible for a Convolutional Neural Network to output "no classification" for a multi-audio classifier? Is it common practice to set % thresholding to the output vector of probabilities?
I am building a CNN audio classifier that is able to detect chicken chirps in raw audio data. So far I have trained the model based off of labeled segmented audio of chicken chirps. The model performs well on the validation set of similar segmented audio with a clear classification.
My goal is to run this model on extended raw audio data that contains both chicken chirps and nothing/ silence and have it classify the time in which the chirps occur.
I have segmented the raw audio data into windows and fed it into the model. The problem though is the output of the CNN model is a vector of classification probabilities. What if there isn't a chirp and its silent? How do I get the model to output "no classifcation" or give no classification at all? Is it possible to set a % threshold on the output vector of probabilities that if above we can confirm a classification or below no classification? Is there a rule of thumb at which this % threshold should be?

Convolutional neural networks do not output classifications. Common software tools have emphasized categorical predictions, but those are based on neural network outputs that exist on a continuum.
You can do whatever you want with those predictions. If it makes sense to classify everything with a $90\%$ chance of being a particular class as being in that class, while reporting “unknown” otherwise, then go for it!
How you choose that threshold (or multiple thresholds) will depend on your problem and the costs of mistakes. The threshold also does not have to be the same for every input. You might have certain inputs where you are more or less willing to classify in the “unknown” category.
Related: Proper scoring rule when there is a decision to make (e.g. spam vs ham email)
