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Sam Perry
2016-08-27 11:37:39 +01:00
parent 8a7eec0900
commit 13f6c476d8
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\setkomafont{subsubsection}{\normalfont\fontsize{12}{17}\itshape}
\graphicspath{{./resources/}}
\addbibresource{~/PerryPerrySource/LaTeX/library.bib}
\addbibresource{~/Documents/library.bib}
\usepackage{etoolbox}
\makeatletter
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samples that have been directly recorded from a source, the subtle nuances
of the source's sound are preserved. These would be difficult to reproduce
using other synthetic methods for modeling an
instrument~\parencite[p.24]{Maestre2009a}.
instrument~\parencite[p.24]{Maestre2009}.
\subsection*{Speech Synthesis}
Creating a natural and intelligible realisation is an important factor when
@@ -188,20 +188,38 @@
the framework was written in the Python programming language. Python has
grown in popularity in the scientific community recently, primarily due to
it's focus on productivity, readability and the large number of efficient
numeric processing libraries (Numpy, SciPy, Scikitlearn etc...)
available~\parencite[p.11]{Fangohr2014}. This makes Python a good choice
for quickly developing ideas in the context of audio signal processing.
numeric processing libraries available (Numpy, SciPy, Scikitlearn
etc...)~\parencite[p.11]{Fangohr2014}. This makes Python a good choice for
quickly developing ideas in the context of audio signal processing.
Unfortunatley, the language does sacrafice processing speed for simplicity
and as a result is not suitable for real-time signal processing. Other
performance focused languages such as C++ are better suited to this type of
processing. However, it was decided that the increase in productivity, lack
of prior CS research and the author's previous experience in the language,
made it the most suitable choice for this project.
Offline processing to allow for large databases to be used - disadvantage: loss of feedback between performer and system, as described in PA's paper.
advantage: Real-time approach results in reduced continuity of grains
of prior CS research and the author's previous experience in Python,
made it the most suitable choice for this project.\\
The choice to limit the project to offline processing has both positive and
negative implications on the function of the project. A key disadvantage to
this type of processing is the lack of possibility for any live performance
aspect. This method provides no way of exploring the feedback between
performer and system in a live environment, comparable to the work of
Tremblay and Schwarz's~\citeyearpar{Tremblay2010}.
However, there are advantages to offline processing that would not be
possible in a real-time context.\\
One significant advantage is that databases can afford to be far larger
than they could in real time. Without the requirement to process output in
a short period of time, more time can be taken to search vast databases in
the hope that the closest match to a target will be found.\\
Another advantage is in the global view of a target that can be taken in an
offline approach. Because the complete audio file is available from the
start of processing, techniques can be applied that consider the output as
a whole rather than on a grain by grain basis. This allows for algorithms
such as the viterbi algorithm to find the sequence of grains that provide
the best continuity, as demonstarted in the Catapillar
project~\parencite[p.4]{Schwarz2003} This would not be possible in
real-time, as audio is processed on the fly.
instrument resynthesis onto a pre-existing source sound, rather than from scratch onto things like midi notes.