I'm trying to make a "car sound detector" I have the data from https://urbansounddataset.weebly.com/urbansound.html site. Which has 1000 labeled sounds for 10 different classes(Car sound, dog bark, drill..etc). So using a binary classifier I want to detect if a given sound sample is a car -> label 1 or any other sound from that data set -> label 0.
So data is highly unbalanced. 1000 1-labeled car sounds against 9000 0-labeled other sounds.
To make a more balanced set, for example in both test and training sets I can create %50 / %50 split using oversampling(not sure its a good idea). But in a real life scenario (a microphone listening environment sounds) the ratio of a car sounds is not gonna be %50. Maybe for every 100 sounds the mic picked only one percent will be a car sound.
So what would be a better/realistic to way to prepare and split the data for test and training set?