Verlagslink: https://nbn-resolving.org/urn:nbn:de:gbv:8:3-2026-01081-2
Verlagslink DOI: 10.38071/2026-01081-2
Titel: Development of a hexaphonic pickup for electric guitars and conversion of analog sound into the MIDI format
Sprache: Englisch
Autorenschaft: Hensel, Marc  
Vogel, Fiona 
Herausgeber*In: Heute, Felix 
Meißer, Michael 
Herausgeber: Christian-Albrechts-Universität zu Kiel. Technische Fakultät 
Schlagwörter: Electric guitar; Hexaphonic pickup; Audio signal processing; MIDI
Erscheinungsdatum: 2026
Verlag: Kiel University, Faculty of Engineering, Department of Electrical and Information Engineering (ET&IT)
Teil der Schriftenreihe: StuFoTech : conference for student research in the field of electrical engineering in relation to environment and society 
Wird ergänzt von: 10.38071/2026-01081-2
Konferenz: Konferenz für Studentische Forschung im Spannungsfeld Elektrotechnik 2025 
Zusammenfassung: 
Unlike conventional pickups for electric guitars, hexaphonic pickups generate a separate signal for each guitar string instead of a single combined signal for all six strings. This simplifies the task to convert analog guitar signals to the prevalent MIDI format for music notation and communication between digital music instruments and control devices.

In this context, a hexaphonic pickup for electric guitars together with a complete hardware and software processing chain from sound generation to pitch detection has been developed. A first functional, but mechanically instable, pickup was used to record soundtracks representing individual strings to audio files. These files were analyzed and successfully converted to the MIDI format. In parallel, the initial design and manufacturing process of the pickup was improved.

The work serves as proof of concept that monophonic pitch detection can work quasi-polyphonic when using hexaphonic pickups. Moreover, the pickup and principal audio chain provide a solid platform for future works in the fields of, for instance, computer science, electrical engineering, applications related to music, and in general classical signal processing as well as deep learning applied to parallel time signals.
URI: https://hdl.handle.net/20.500.12738/19925
Begutachtungsstatus: Diese Version hat ein Peer-Review-Verfahren durchlaufen (Peer Review)
Einrichtung: Fakultät für Elektro-, Medien- und Informationstechnik 
Dokumenttyp: Konferenzveröffentlichung
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