Pitch Perfect

There's something rather fitting about finding your programming salvation at the bottom of a laundry basket. Not that it had been there for twenty-five years, mind you – I'm not quite that slovenly. But when the moment arrived to resurrect Igor Engraver as the open-source project now becoming Ooloi, I suddenly realised that the only piece of original code I possessed was printed on a promotional t-shirt from 1996.

The search was frantic. I'd just committed to rebuilding everything from scratch: Common Lisp to Clojure, QuickDraw GX to modern graphics, the whole shebang. Yet somewhere in my flat lay a single fragment of the original system, a higher-order function for creating pitch transposers that I dimly recalled being rather important. After tearing through a hundred-odd t-shirts (mostly black, naturally), I found it crumpled beneath a pile of equally rumpled garments.

The print quality had survived remarkably well. More remarkably still, when I a few days ago, after a year of implementing the Ooloi engine, fed the photographed code to ChatGPT 5, it immediately identified this transposer factory as the architectural cornerstone of Igor Engraver. That was both validating and slightly unnerving: I'd forgotten precisely how central this code was, but an AI recognised its significance instantly.

I clearly had chosen this piece of code for this very reason. And as LLMs are multidimensional concept proximity detectors, the AI immediately saw the connection. Now it was up to me to transform and re-implement this keystone algorithm.

The Dread of Understanding

I'd glimpsed this code periodically over the years, but I'd never truly penetrated it. There were mysterious elements – that enigmatic 50/51 cent calculation, for instance – that I simply didn't grasp. The prospect of reimplementing it filled me with a peculiar dread. Not because it was impossibly complex, but because I knew I'd have to genuinely understand every nuance this time.

Pitch representation sits at the absolute heart of any serious music notation system. Get it wrong, and everything else becomes compromised. Transposition, particularly diatonic transposition, must preserve musical relationships with mathematical precision whilst maintaining notational correctness. A piece requiring a progression from C𝄪 to D𝄪 cannot tolerate a system that produces C𝄪 to E♮, regardless of enharmonic equivalence. The spelling matters profoundly in musical contexts.

And then there's the microtonal dimension. Back in 1996, no notation software could actually play microtonal music, even if some of them could display quarter-tone symbols. Igor Engraver was different:  our program icon featured a quarter-tone natural symbol (𝄮) for precisely this reason. My original intended audience consisted primarily of contemporary art music composers who needed these capabilities. I needed them myself.

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MIDI Sorcery

​Our solution was elegantly brutal: we seized complete control of attached MIDI units and employed pitch bend to achieve microtonal accuracy. This required distributing notes across MIDI channels according to their pitch bend requirements, using register allocation algorithms borrowed from compiler technology. In a chord containing one microtonally altered note, that note would play on a different channel from its companions. We changed patches frantically and maintained no fixed relationship between instruments and channels – everything existed in a kind of 'DNA soup' where resources were allocated dynamically as needed.

This approach let us extract far more than the nominal sixteen-channel limit from typical MIDI synthesisers. We maintained detailed specifications for every common synthesiser on the market, including how to balance dynamics and handle idiosyncratic behaviours. 

Real-World Musical Intelligence

​The system's sophistication extended well beyond pure pitch calculations. When my opera The Maids was commissioned by the Royal Stockholm Opera, I spent considerable time crafting realistic rehearsal tapes. Everything I learned from that process was automated into Igor's playback engine.

We also collaborated with the KTH Royal Institute of Technology Musical Acoustics department, led by the legendary Johan Sundberg, whose research had quantified subtle but crucial performance characteristics. Those famous four milliseconds – the consistent temporal offset between soloists and accompaniment in professional orchestras – found their way into our algorithms. Such details proved particularly effective with Schönberg's Hauptstimme markings (𝆦) or similar solo indicators.

We also developed what my composer colleague Anders Hillborg and I privately called 'first performance prophylaxis' – a deliciously cruel setting that simulated the sound of musicians who hadn't practiced. In other words, the kind of sound landscape any composer is used to hearing at a first orchestral rehearsal of a new piece and which always makes you doubt your own talent. Turn this setting up, and you'd hear a characteristically dreadful youth orchestra. Turn it down completely, and you'd get the robotic precision that plagued every other MIDI system. Rather like Karl Richter's Baroque organ recordings.

The humanisation algorithms incorporated realistic instrumental limitations. Passages written too quickly for an instrument would skip notes convincingly. We modelled the typical rhythmic hierarchy of orchestral sections: percussion most precise, then brass, then woodwinds, with strings bringing up the rear. Instruments were panned to their proper orchestral seating positions. Piccolo trills were faster than tuba trills. The result was startlingly realistic, particularly by 1996 standards.

The ADR and Current Reality

​Now, twenty-five years later, that laundry basket discovery has culminated in ADR 0026: Pitch Representation and Operations, documenting Ooloi's comprehensive pitch representation system. The original Common Lisp has been reborn as Clojure code, with string-based pitch notation ("C#4+25") serving as the canonical format and a factory-based transposition system supporting both chromatic and diatonic modes.

The string representation offers several advantages: compact memory usage for large orchestral scores, direct human readability for debugging, and seamless integration with parsing and caching systems. Most crucially, it supports arbitrary microtonal deviations, something that remains problematic in most contemporary notation software.

The factory pattern generates specialised transposition functions that encapsulate their musical behavior rules through closures. Rather than repeatedly passing configuration parameters, the factory creates efficient, composable functions that understand their specific musical contexts. A diatonic transposer preserves letter-name relationships; a chromatic transposer produces frequency-accurate results with canonical spellings.

Closure

The t-shirt in my laundry basket represented more than nostalgic memorabilia; it was unfinished business. That higher-order function embodied a sophisticated understanding of musical mathematics that took a long time to develop and seconds for an AI to recognise as architecturally significant.

Now, with Ooloi's pitch operations properly documented and implemented, that business approaches completion. The code has evolved from promotional garment to production system, carrying forward those insights from 25 years ago into a new, modern technological context.

It's exciting. And still a little unnerving.