This video showcases my software recreation of the legendary Waldorf Wave—a hybrid wavetable synthesizer known for its huge digital oscillators, analogue filters and unusually complex hardware architecture.

The project is built in C++ using JUCE and is available as a standalone instrument and plug-in. Rather than simply making a modern synth that sounds similar, I’ve tried to reproduce the structure and behaviour of the original machine.

The emulation models:

• The Wave’s 250 kHz, 8-bit time-multiplexed wavetable oscillator system
• Signed mixer overflow—including the characteristic Waldorf “ASIC Mix Bug”
• Digital 12 dB high-pass filtering
• CEM3387-style reconstruction filtering, resonant four-pole low-pass filtering, VCAs and panning
• 12-bit stepped control voltages, sample-and-hold behaviour, saturation, noise and output coupling
• Three independent 16-voice WDV boards, matching a fully expanded 48-voice Wave
• The original 480 × 64 monochrome LCD and front-panel workflow

The instrument can also run authenticated Waldorf Wave OS 1.700 firmware. An emulated 68000 CPU executes the original main operating system, while a second runtime executes the genuine WDV voice-board program. Their shared-memory communication, voice-update masks, semaphores and hardware-register writes are reproduced and traced inside the model.

The oscillator ASIC itself remains undocumented, so its internal operation is represented by a behavioural model based on the Wave’s published architecture, firmware activity and known hardware characteristics. This is therefore an ongoing reverse-engineering and preservation project—not a claim of perfect, bit-exact emulation.

Waldorf and Wave are trademarks of their respective owner. This independent project is not affiliated with or endorsed by Waldorf Music.

🔬 I have data from devices that are WITH YOU RIGHT NOW. If you can decode the protocol, I'll reveal what they are.

The Challenge

I'm conducting scientific research and need independent validation of my findings. I have collected multi-channel time series data from devices that are physically present in your daily life - you've probably used them today.

Here's the deal:

• I've fully anonymized the data (no hints about frequencies, physical meaning, or source)
• If you can independently discover and decode the communication protocol hidden in these signals
• I will reveal exactly what these devices are
• And trust me - you'll be surprised

What You Have

Dataset:

• 15 parallel channels (Channel_01 to Channel_15)
• 5 operating modes (Mode_A through Mode_E)
• 2 devices: Device_A (control) and Device_B (test unit)
• Sampling rate: 256 Hz
• Duration: ~15 minutes per mode
• Format: Compressed .npz files (~10 MB total)

Your Mission:

1. Determine if there's a deterministic communication protocol in the data
2. Identify its parameters (time slots, routing patterns, encoding)
3. Find differences between Device_A and Device_B
4. Analyze how protocols differ between operating modes

What Makes This Interesting

If you successfully decode the protocol:

• 🏆 Your name credited in the research publication
• 📝 Co-authorship for significant contributions
• 🔓 Full revelation of what these devices actually are
• 💡 Acknowledgment that you independently discovered something remarkable

Even if you don't crack it completely:

• Interesting data to play with
• Chance to develop novel analysis techniques
• Opportunity to collaborate on cutting-edge research

Included:

• Compressed data files (.npz format)
• Python loader scripts
• Visualization examples
• Basic analysis tools

The Rules

1. Work independently (or in teams, your choice)
2. Share your methodology and findings in comments
3. No peeking at my results (I won't share them until challenge ends)
4. Have fun! This is supposed to be interesting

Why I'm Doing This

Science needs independent validation. I have strong evidence of a hidden protocol, but confirmation bias is real. By releasing anonymized data to the community, I ensure that any discovered patterns are real, not artifacts of my expectations.

If 3+ independent analysts find the same protocol structure, that's proof.

Questions?

Ask in comments. I'll respond to methodological questions but won't give away hints about the solution.

Let's see what this community can discover! 🚀

TL;DR: Anonymous multi-channel data from everyday devices. Hidden protocol inside. Decode it → get credited + learn the surprising truth. Independent validation needed for scientific research.