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dc.contributor.authorStowell, Dan
dc.date.accessioned2011-01-27T14:14:54Z
dc.date.available2011-01-27T14:14:54Z
dc.date.issued2010
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/412
dc.descriptionPhDen_US
dc.description.abstractPeople can achieve rich musical expression through vocal sound { see for example human beatboxing, which achieves a wide timbral variety through a range of extended techniques. Yet the vocal modality is under-exploited as a controller for music systems. If we can analyse a vocal performance suitably in real time, then this information could be used to create voice-based interfaces with the potential for intuitive and ful lling levels of expressive control. Conversely, many modern techniques for music synthesis do not imply any particular interface. Should a given parameter be controlled via a MIDI keyboard, or a slider/fader, or a rotary dial? Automatic vocal analysis could provide a fruitful basis for expressive interfaces to such electronic musical instruments. The principal questions in applying vocal-based control are how to extract musically meaningful information from the voice signal in real time, and how to convert that information suitably into control data. In this thesis we address these questions, with a focus on timbral control, and in particular we develop approaches that can be used with a wide variety of musical instruments by applying machine learning techniques to automatically derive the mappings between expressive audio input and control output. The vocal audio signal is construed to include a broad range of expression, in particular encompassing the extended techniques used in human beatboxing. The central contribution of this work is the application of supervised and unsupervised machine learning techniques to automatically map vocal timbre to synthesiser timbre and controls. Component contributions include a delayed decision-making strategy for low-latency sound classi cation, a regression-tree method to learn associations between regions of two unlabelled datasets, a fast estimator of multidimensional di erential entropy and a qualitative method for evaluating musical interfaces based on discourse analysis.en_US
dc.language.isoenen_US
dc.subjectElectronic Engineeringen_US
dc.titleMaking music through real-time voice timbre analysis: machine learning and timbral controlen_US
dc.typeThesisen_US
dc.rights.holderThis work is copyright c2010 Dan Stowell, and is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported Licence. To view a copy of this licence, visit http://creativecommons.org/licenses/by-sa/3.0/


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  • Theses [2960]
    Theses Awarded by Queen Mary University of London

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