Learning new instruments is never a simple task on your own; nothing can beat the instant feedback of a teacher. In our new age of AI, why not have an AI companion complain when you’re off note? This is exactly what [Ada López] put together with their AI-Powered Piano Trainer.

The basics of the piano rely on rather simple boolean actions, either you press a key or not. Obviously, this sets up the piano for many fun projects, such as creative doorbells or helpful AI models. [Ada López] started their AI model with a custom dataset with images of playing specific notes on the piano. These images then get fed into Roboflow and trained using the YOLOv8 model.

Using the piano training has the model run on a laptop and only has a Raspberry Pi for video, and gives instant feedback to the pianist due to the demands of the model. Placing the Pi and an LCD screen for feedback into a simple enclosure allows the easy viewing of how good an AI model thinks you play piano. [Ada López] demos their device by playing Twinkle Twinkle Little Star but there is no reason why other songs couldn’t be added!

While there are simpler piano trainers out there relying on audio cues, this project presents a great opportunity for a fun project for anyone else wanting to take up the baton. If you want to get a little more from having to do less in the physical space, then this invisible piano is perfect for you!

Think of a circuit model that lets you move magnetic leakage around like sliders on a synth, without changing the external behavior of your coupled inductors. [Sam Ben-Yaakov] walks you through just that in his video ‘Versatile Coupled Inductor Circuit Model and Examples of Its Use’.

The core idea is as follows. Coupled inductors can be modeled in dozens of ways, but this one adds a twist: a tunable parameter 𝑥 between k and ₁ (where k is the coupling coefficient). This fourth degree of freedom doesn’t change L_₁, L_₂ or mutual inductance M (they remain invariant) but it lets you shuffle leakage where you want it, giving practical flexibility in designing or simulating transformers, converters, or filters with asymmetric behavior.

If you need leakage on one side only, set 𝑥=k. Prefer symmetrical split? Set 𝑥=1. It’s like parametric EQ, but magnetic. And: the maths holds up. As [Sam Ben-Yaakov] derives and confirms that for any 𝑥 in the range, external characteristics remain identical.

It’s especially useful when testing edge cases, or explaining inductive quirks that don’t behave quite like ideal transformers should. A good model to stash in your toolbox.

As we’ve seen previously, [Sam Ben-Yaakov] is at home when it comes to concepts that need tinkering, trial and error, and a dash of visuals to convey.