| dc.description.abstract | People 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 |
