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dc.contributor.supervisorMiranda, Eduardo
dc.contributor.authorAntoine, Aurélien
dc.contributor.otherFaculty of Arts, Humanities and Businessen_US
dc.date.accessioned2018-07-30T11:32:59Z
dc.date.issued2018
dc.identifier10465036en_US
dc.identifier.urihttp://hdl.handle.net/10026.1/11959
dc.description.abstract

Researchers have investigated harnessing computers as a tool to aid in the composition of music for over 70 years. In major part, such research has focused on creating algorithms to work with pitches and rhythm, which has resulted in a selection of sophisticated systems. Although the musical possibilities of these systems are vast, they are not directly considering another important characteristic of sound. Timbre can be defined as all the sound attributes, except pitch, loudness and duration, which allow us to distinguish and recognize that two sounds are dissimilar. This feature plays an essential role in combining instruments as it involves mixing instrumental properties to create unique textures conveying specific sonic qualities. Within this thesis, we explore harnessing techniques for the analysis and control of instrumental timbre and timbral combinations.

This thesis begins with investigating the link between musical timbre, auditory perception and psychoacoustics for sounds emerging from instrument mixtures. It resulted in choosing to use verbal descriptors of timbral qualities to represent auditory perception of instrument combination sounds. Therefore, this thesis reports on the developments of methods and tools designed to automatically retrieve and identify perceptual qualities of timbre within audio files, using specific musical acoustic features and artificial intelligence algorithms. Different perceptual experiments have been conducted to evaluate the correlation between selected acoustics cues and humans' perception. Results of these evaluations confirmed the potential and suitability of the presented approaches. Finally, these developments have helped to design a perceptually-orientated generative system harnessing aspects of artificial intelligence to combine sampled instrument notes.

The findings of this exploration demonstrate that an artificial intelligence approach can help to harness the perceptual aspect of instrumental timbre and timbral combinations. This investigation suggests that established methods of measuring timbral qualities, based on a diverse selection of sounds, also work for sounds created by combining instrument notes. The development of tools designed to automatically retrieve and identify perceptual qualities of timbre also helped in designing a comparative scale that goes towards standardising metrics for comparing timbral attributes. Finally, this research demonstrates that perceptual characteristics of timbral qualities, using verbal descriptors as a representation, can be implemented in an intelligent computing system designed to combine sampled instrument notes conveying specific perceptual qualities.

en_US
dc.description.sponsorshipArts and Humanities Research Council funded 3D3 Centre for Doctoral Trainingen_US
dc.language.isoen
dc.publisherUniversity of Plymouth
dc.subjectTimbreen_US
dc.subjectMachine Learningen_US
dc.subjectInstrument Combinationen_US
dc.subjectPsychoacousticsen_US
dc.subjectTimbral Combinationen_US
dc.subjectAudio Analysisen_US
dc.subjectAuditory Perceptionen_US
dc.subjectComputer Musicen_US
dc.subject.classificationPhDen_US
dc.titleAn Investigation into the Use of Artificial Intelligence Techniques for the Analysis and Control of Instrumental Timbre and Timbral Combinationsen_US
dc.typeThesis
plymouth.versionpublishableen_US
dc.identifier.doihttp://dx.doi.org/10.24382/1189
dc.rights.embargodate2019-07-30T00:00:00Z
dc.rights.embargoperiod12 monthsen_US
dc.type.qualificationDoctorateen_US
rioxxterms.versionNA
plymouth.orcid_idhttp://orcid.org/0000-0002-5319-3534en_US


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