How mic setting will affect audio classification model?
We can't tell you what your classifier does, sorry.

But yes, you have a systematic bias in your data, and your classifier will cling to whatever is the strongest discriminator if it works as hoped. I will rename your classes to make this clearer:


100 audio samples : class 'Microphone 1 subclass 1'
100 audio samples : class 'Microphone 1 subclass 2'
100 audio samples : class 'Microphone 2'


If that strongest discriminator is the microphone used in the downsampled signals (and that is not an unreasonable assumption to make), then yes, your classifier performance will depend on the microphone used.

The "properest" way would be to go out and acquire some of class 'A' and 'B' with microphone 2; but assuming that is unreasonably much work: you could try playing back all sounds and record them through both microphones, and use random batches so that the you don't only see class C with the properties of microphone 2, but also classes A and B through 2, and C through 1. Maybe that is sufficient to reduce the role of the microphone characteristics for classification.

But really, you have a class that is really identical with being recorded via microphone 2, and your choice to call that class "C" instead of "recordings done with microphone 2" is your choice alone!