The synthesizer programming problem: improving the usability of sound synthesizers

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dc.contributor.authorShier, Jordie
dc.contributor.supervisorTzanetakis, George
dc.contributor.supervisorMcNally, Kirk
dc.date.accessioned2021-12-16T00:23:45Z
dc.date.available2021-12-16T00:23:45Z
dc.date.copyright2021en_US
dc.date.issued2021-12-15
dc.degree.departmentInterdisciplinary Graduate Programen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe sound synthesizer is an electronic musical instrument that has become commonplace in audio production for music, film, television and video games. Despite its widespread use, creating new sounds on a synthesizer - referred to as synthesizer programming - is a complex task that can impede the creative process. The primary aim of this thesis is to support the development of techniques to assist synthesizer users to more easily achieve their creative goals. One of the main focuses is the development and evaluation of algorithms for inverse synthesis, a technique that involves the prediction of synthesizer parameters to match a target sound. Deep learning and evolutionary programming techniques are compared on a baseline FM synthesis problem and a novel hybrid approach is presented that produces high quality results in less than half the computation time of a state-of-the-art genetic algorithm. Another focus is the development of intuitive user interfaces that encourage novice users to engage with synthesizers and learn the relationship between synthesizer parameters and the associated auditory result. To this end, a novel interface (Synth Explorer) is introduced that uses a visual representation of synthesizer sounds on a two-dimensional layout. An additional focus of this thesis is to support further research in automatic synthesizer programming. An open-source library (SpiegeLib) has been developed to support reproducibility, sharing, and evaluation of techniques for inverse synthesis. Additionally, a large-scale dataset of one billion sounds paired with synthesizer parameters (synth1B1) and a GPU-enabled modular synthesizer (torchsynth) are also introduced to support further exploration of the complex relationship between synthesizer parameters and auditory results.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationShier, Jordie, Kirk McNally, George Tzanetakis, and Ky Grace Brooks. "Manifold Learning Methods for Visualization and Browsing of Drum Machine Samples." Journal of the Audio Engineering Society 69, no. 1/2 (2021): 40-53.en_US
dc.identifier.bibliographicCitationShier, Jordie, George Tzanetakis, and Kirk McNally. "SpiegeLib: An automatic synthesizer programming library." In Audio Engineering Society Convention 148. Audio Engineering Society, 2020.en_US
dc.identifier.bibliographicCitationTurian, Joseph, Jordie Shier, George Tzanetakis, Kirk McNally, and Max Henry. "One Billion Audio Sounds from GPU-enabled Modular Synthesis." arXiv preprint arXiv:2104.12922 (2021).en_US
dc.identifier.bibliographicCitationShier, Jordie, Kirk McNally, and George Tzanetakis. "Analysis of Drum Machine Kick and Snare Sounds." In Audio Engineering Society Convention 143. Audio Engineering Society, 2017.en_US
dc.identifier.bibliographicCitationShier, Jordie, Kirk McNally, and George Tzanetakis. "Sieve: A plugin for the automatic classification and intelligent browsing of kick and snare samples." In Proceedings of the 3rd Workshop on Intelligent Music Production. 2017.en_US
dc.identifier.urihttp://hdl.handle.net/1828/13593
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectmusicen_US
dc.subjectaudio synthesisen_US
dc.subjectsynthesizeren_US
dc.subjectmachine learningen_US
dc.subjectcomputational creativityen_US
dc.subjectmusic information retrievalen_US
dc.subjectartificial intelligenceen_US
dc.subjectdigital signal processingen_US
dc.subjectcreativity support toolsen_US
dc.titleThe synthesizer programming problem: improving the usability of sound synthesizersen_US
dc.typeThesisen_US

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