Maneco Labs took the concept of a drone machine and packed it into a desktop pedal format, which is a pretty bold move considering how much sonic territory they wanted to cover. The result is something that feels less like a traditional effects unit and more like a complete synthesizer voice that happens to process external audio.
The core of the Grone is a 16-algorithm digital oscillator that generates sound from mathematical equations converted to analog signals. You navigate between algorithms using up and down switches, and three pots let you sculpt the character of each equation by modifying their parameters. A fourth knob controls sample rate, which shifts both pitch and the speed of oscillation, letting you dial in everything from smooth drones to glitchy, degraded textures. The signal path runs through an MS-20 style resonant lowpass filter with a prominent cutoff knob, resonance control, and an LFO that's normalized to modulate the filter. The LFO itself offers eight waveforms including ramps, triangle, sine, sweep, and random variations. A built-in noise generator adds another layer, and you can toggle the oscillator, filter, and Clouds sections independently with dedicated footswitches. Two expression pedal inputs give you real-time control over sample rate and filter cutoff, making it genuinely playable as a performance tool.
What really sets the Grone apart is the inclusion of Clouds, Mutable Instruments' granular processor, running the Parasites firmware which adds a resonator and reverb algorithms beyond the original. You get full parameter control via knobs, and a large pitch knob dominates the Clouds section. An audio input lets you feed external signals through the filter and Clouds, or blend them with the internal oscillator for layered textures. The pedal measures 6 by 5 inches and runs on a standard 9V center-negative power supply, making it compact enough to integrate into a pedalboard or sit standalone on a desk. Musicians have gravitated toward the Grone for its immediacy and depth, appreciating how quickly you can stumble into compelling soundscapes while still having room to dig deeper into each algorithm's character.
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