For audio based detection, I would start with collecting a large corpus of normal shows vs ad segment audio dataset. I assume the amplitude (volume changes) and frequency distribution would be unique enough to distinguish between them.

Train a model using their Mel Spectrogram and deploy on-device.

Microphone -> ADC -> Preprocessing -> Spectrogram Generation -> Inference -> Mute/Unmute

Would be an interesting project.