Whilst I am working on re-designing the PCB, to use the correct esp32-d0wd footprint, I am also going to investigate making use of TensorFlow via Python, to recognise different acoustic events in a house.

To do this I will record various sounds in a house using a microphone and a laptop, I will then attempt to classify these events using TensorFlow.  I was planning on looking into using a convolutional neural network to achieve this.

Some of the acoustic events I will attempt to detect, include:

• Door opening
• Tap running
• Light / mains switch being turned on/off
• Smoke alarm
• Blender
• Gas hobs on oven being turned on
• Phone ringing
• Microwave oven / standard oven beep
• Kettle finished boiling
• Fridge turning on
• Toaster popping

I am also curious if an end user could train the system to detect new events by allowing the classifier to be updated.

I've just started evaluating the classifier from https://github.com/drscotthawley/panotti/ using sound files for smoke alarms etc. from - https://freesound.org/browse/ .  It makes use of TensorFlow for creating the model.