Science Meets Sound

Our process is equal parts mathematical rigor and hands-on musicianship. We prototype DSP in C++, model circuits in MATLAB, and A/B test against analog benchmarks. Expect advanced filtering, zero-latency processing, and precision metering—built for pros, tested by artists.

// Fast, thread-safe DSP in C++ (example: one-pole filter)
inline float OnePole::process(float input) noexcept {
    z = input * a0 + z * b1;
    return z;
}

Frameworks & Compatibility

We use JUCE as our backbone—delivering cross-platform plugins for Windows, Mac, and Linux, in VST3, AU, and AAX formats. Need custom DSP? We code from scratch. Want fast prototyping? Our hybrid workflow leverages Python, Faust, and C++ for rapid iteration and gold-standard performance.

// Minimal JUCE plugin boilerplate (processBlock)
void PluginProcessor::processBlock(AudioBuffer<float>& buffer, MidiBuffer& midiMessages) {
    for (int ch = 0; ch < buffer.getNumChannels(); ++ch)
        for (int s = 0; s < buffer.getNumSamples(); ++s)
            buffer.setSample(ch, s, buffer.getSample(ch, s) * gain);
}

Case Study: Reinventing Metering

Project: Next-gen “BottomEnder” plugin for mixing bass
Challenge: Visualize low end for any listening environment, in real time, with zero latency
Solution: We built a JUCE plugin with custom FFT routines and LED-style visualization. User-set “listener profiles” emulate car stereos, earbuds, or pro monitors.
Result: Pro mixers get instant, actionable feedback—no more guesswork, better mixes, less fatigue.

Case Study: Automated Drum Replacer

Project: Real-time snare/kick trigger for DAWs
Challenge: Detect and replace drum hits with near-zero latency
Solution: Hybrid Python/C++ algorithm—ML-powered onset detection, user sample mapping, all inside a JUCE shell.
Result: Seamless studio workflow, tight timing, huge sound. Artists save hours per session.