Shkolotron & CircuitFox
Shkolotron Shkolotron
CircuitFox CircuitFox
Shkolotron Shkolotron
Hey CircuitFox, I've been noodling on the idea of a microcontroller that can compose music on the fly based on what it hears—a neural synth that writes its own patches. What do you think, could that be a cool project?
CircuitFox CircuitFox
Yeah, that’s exactly the kind of thing that gets me excited—take a microcontroller, feed it raw audio, run a lightweight neural net, and let it spit out patch parameters in real time. Just imagine tweaking the weight updates while it’s composing; the more it listens, the more it learns its own sonic vocabulary. It’ll be a little messy, but that’s where the fun is. Let’s sketch the architecture first, then dive into the code.
Shkolotron Shkolotron
Cool—so we’ll need a real‑time audio capture block, a tiny DSP pipeline, a neural net in something like TensorFlow Lite for Microcontrollers, and an interface to the synth module. The net could take, say, a 256‑sample window, output a vector of patch knobs, then feed that to a patch generator. We’ll also need a learning loop that tweaks weights based on a simple reward, maybe the change in loudness or user clicks. Ready to sketch the data flow?
CircuitFox CircuitFox
Absolutely, let’s map it out step by step: audio capture feeds 256‑sample frames into a DSP buffer, the DSP does a quick FFT and extracts features, those go into a tiny TFLM net, the net outputs a patch vector, that vector drives a patch generator module, and the synth outputs sound. Meanwhile, the learning loop watches the output, maybe tracks loudness changes or user‑clicks, and nudges the weights a bit each cycle. We’ll need to keep the net small enough for the MCU, maybe a couple of dense layers, and use quantization to fit. Ready to write the block diagram?
Shkolotron Shkolotron
Sure thing. Picture this: 1. **Microphone → ADC** – 16‑bit, 48 kHz, feeds a ring buffer. 2. **DSP Block** – grabs 256 samples, does an FFT, pulls magnitude & spectral‑centroid features. 3. **Feature Vector → Tiny Neural Net (TFLM)** – two 64‑node dense layers, quantized to 8‑bit. 4. **Net Output** – 12‑dimensional vector of knob values (filter cutoff, resonance, envelope times, etc.). 5. **Patch Generator** – maps those values onto synth parameters in real time. 6. **Synth Engine** – outputs audio to DAC. 7. **Feedback Loop** – monitors loudness or a button click, computes a simple loss, and runs a tiny gradient step on the net weights. That’s the skeleton—let me know which part you want to flesh out first.
CircuitFox CircuitFox
Let’s start with the DSP block, because that’s the gateway to everything else. Grab 256 samples from the ring buffer, run a 256‑point FFT with CMSIS‑DSP, then pull out the magnitude spectrum and a quick spectral‑centroid or spectral‑flux feature. Pack those into a 12‑element float vector (or quantize to 8‑bit right away). That way the data is clean and small before it hits the TFLM net. Once we have that pipeline solid, we can dive into the network architecture and weight‑update code. Sound good?
Shkolotron Shkolotron
Sounds solid—just remember to double‑check the ring‑buffer wrap logic before you fire up the FFT, otherwise you’ll feed the net garbage from the first frame. Once you’ve got the 12‑element vector clean, we can start pruning the network. Ready to dive into the actual code?
CircuitFox CircuitFox
Yeah, the wrap‑around can kill the FFT if you’re not careful, so I’ll double‑check that first frame. Once the 12‑element feature vector is reliable, we’ll slice the network—maybe cut the first dense layer to 32 nodes, keep the second at 16, then prune weights that stay close to zero. I’ll start writing the ring buffer handler and the CMSIS‑FFT call, then we can plug the TFLM inference right after. Ready?
Shkolotron Shkolotron
Great, just make sure your ring buffer index resets cleanly—no half‑filled frames on start—and I’ll be here to poke holes in the FFT code or suggest a trick to keep the 8‑bit quantization from blowing up the spectral flux. Let's roll.
CircuitFox CircuitFox
Got it, I’ll zero the ring buffer pointers on init and only pull a full 256‑sample frame when the count hits 256, so no half‑filled garbage. For 8‑bit quantization, I’ll normalize the spectral‑flux to a 0‑255 range before feeding the net, and clamp any out‑of‑range values—keeps the weights from blowing up. Let’s fire up the FFT and see if the net starts learning something interesting.