awesome-teachable-machine VS BirdNET-Pi

Not sure if I've seen anything of the sort, seems rather specific. Maybe try a Teachable Machine project? https://teachablemachine.withgoogle.com/

Train a computer to recognize your images, sounds, and poses. Use this resource to gain a better understanding.

[Teachable Machine](https://teachablemachine.withgoogle.com/)

We will create an machine learning model that can classify Arabic and English books. To collect, train, and test data, we will use Teachable Machine from Google.

a lot of places! but for a high schooler, better to focus at what you want to do first. or if you still haven't gotten any idea, try a simple explanation on what ml is without the math on youtube and tinker around a no code machine learning platform like https://teachablemachine.withgoogle.com/

The principle is roughly the same as it is possible with teachablemachine.withgoogle.com.

You mentioned app, so check this out: https://teachablemachine.withgoogle.com

Here is a similar tool and source

Yup! There's an open source software called BirdNet, and someone packaged it up to make it easy to run on Pis - BirdNet Pi.

I had https://github.com/mcguirepr89/BirdNET-Pi running last summer with great success. Might be worth a look.

BirdNET-Pi

The birders are super ahead of the curve. Check out this Rpi implementation of BirdNet: https://github.com/mcguirepr89/BirdNET-Pi

Some other options for low-cost backyard sound/biodiversity monitoring are:

https://www.openacousticdevices.info/

If you do want to use a raspberry pi and the mics you build, check out this project: https://github.com/mcguirepr89/BirdNET-Pi/

I have I've used rpis for multiple things, but by far the most interesting thing I've done with them is as a [birdsong classifier](https://github.com/mcguirepr89/BirdNET-Pi). That requires an external microphone _of some sort_, and is prone to certain pitfalls (e.g. all sorts of background noises trigger false positives on bittern).