Adaptive Modulation in a Digital Modulation Plugin: Design, Implementation, and Evaluation
Author
Einarsson, Thorvald Johan
Term
4. Term
Education
Publication year
2026
Submitted on
2026-05-26
Pages
25
Abstract
This thesis develops and tests Modality Machine, an adaptive real-time guitar effects plugin built as a VST3 audio plugin in C++ using the JUCE framework (a common toolkit for audio software). The idea is that the plugin listens to the guitar and, in real time, decides whether you are playing single notes or chords, then uses that information as a musical control signal. The study asks whether such a detector can provide control that feels expressive and perceptually coherent to the performer in a guitar effects context. The main technical contribution is a hybrid detector that combines spectral flatness and a log-frequency band-density measure, plus a chord hold mechanism with hysteresis for stability. In simple terms, the system continuously estimates how simple or complex the playing is and outputs a continuous complexity signal. This signal drives, among other things, stereo width: single-note lines remain narrow, while chords are spread wide in the stereo field. Another adaptive mapping ties the amplitude envelope to modulation depth, so effect intensity naturally follows the player’s dynamics. The detector is designed as a general musical control signal, independent of specific effect parameters; the implemented mappings are one musically motivated example among many possible uses. The plugin offers two modulation effects, chorus (a multi-voice widening) and tremolo/panner (rhythmic changes in volume and pan), and a two-part user interface that separates performance controls from a calibration mode showing the detector’s internal state in real time. The work sits within adaptive digital audio effects and new interfaces for musical expression and contributes a new detection architecture, a principle-based interaction design framework for adaptive guitar effects, and a structured evaluation methodology for assessing performer responsiveness in audio systems.
Dette speciale udvikler og afprøver Modality Machine, et adaptivt realtids-effektplugin til guitar, bygget som et VST3-lydplugin i C++ ved hjælp af JUCE-frameworket (et udbredt værktøj til lydsoftware). Idéen er, at plugin’et lytter til guitaren og i realtid afgør, om der spilles enkelttoner eller akkorder, og bruger denne information som et musikalsk kontrolsignal. Specialet undersøger, om en sådan detektor kan give kontrol, der opleves som udtryksfuld og perceptuelt sammenhængende for performeren i en guitareffektkontekst. Det primære tekniske bidrag er en hybrid detektor, der kombinerer spektral fladhed og et log-frekvensbaseret båndtæthedsmål samt en akkord-holdemekanisme med hysterese, så vurderingen er stabil. Kort fortalt estimerer systemet løbende, hvor enkelt eller komplekst spillet er, og producerer et kontinuerligt kompleksitetssignal. Dette signal styrer bl.a. stereobredden: enkelttoner holdes rummeligt smalle, mens akkorder bredes ud i stereobilledet. En anden adaptiv kobling lader amplitudekuverten styre modulationsdybden, så effekten reagerer naturligt på spillerens dynamik. Detektoren er designet som et generelt musikalsk kontrolsignal, uafhængigt af bestemte effektparametre; de viste mappings er ét musikalsk motiveret eksempel blandt mange mulige. Plugin’et tilbyder to modulationseffekter, chorus (flere-stemme-lignende bredde) og tremolo/panner (rytmiske volumen- og panoramabevægelser), og en todelt brugerflade med performancekontroller og en kalibreringstilstand, der viser detektorens interne tilstand i realtid. Arbejdet placerer sig inden for adaptive digitale lydeffekter og nye grænseflader for musikalsk udtryk og bidrager med en ny detektionsarkitektur, et principbaseret interaktionsdesignframework for adaptive guitareffekter og en struktureret metode til at evaluere performer-responsivitet i lydsystemer.
[This apstract has been rewritten with the help of AI based on the project's original abstract]
Keywords