Today we’re releasing Transkr V4.
V4 brings together a substantial amount of development in the Transkr engine since the previous release, along with new capabilities that expand what you can do with a transcription. The result is a more capable Transkr for real-time performance, recorded audio, file conversion, MIDI and musical notation.
What happens when a guitar performance stops being just sound and becomes editable musical structure?
That question became the focus of a small live workshop hosted by Algoriffix at Gothenburg’s largest guitar fair, GotGuitars.
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Deep learning did not emerge from a single breakthrough. Long before today’s large-scale AI systems became dominant, researchers were already exploring multilayer representations, convolutions, sparse coding, and hierarchical feature extraction across several overlapping research traditions (see also Before Music AI Became a Commodity).
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Today, much of music AI is dominated by very large models trained on enormous datasets. In many cases, these systems treat audio primarily as a statistical prediction problem: enough data, enough parameters, enough compute, and useful behaviour eventually emerges.
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Much of modern artificial intelligence is built on the assumption that larger models and larger datasets inevitably lead to better systems. In many areas, this approach has produced remarkable results. Music technology, however, presents a somewhat different problem.
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Latency remains one of the central challenges in real-time audio-to-MIDI systems, not because modern computers are too slow, but because musical information itself takes time to emerge from sound.
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Real-time guitar-to-MIDI conversion has fascinated musicians for decades. The idea is simple in principle: play a guitar and control synthesizers, virtual instruments, or notation software directly from the audio signal.
In practice, however, the problem is remarkably difficult.
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