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sounds. This involves the segmentation and analysis of both the input sound
and database, matching of input segments to their closest segment from the
database, and the re-synthesis of the closest matches from the database to
produce the final result. The aim was to produce a tool capable of
produce the final result. The project aims to provide a tool capable of
generating high quality sonic representations of an input, to present a
variety of examples that demonstrated the breadth of possibilities that
this style of synthesis has to offer and to provide a robust framework on
which concatenative synthesis projects could be developed easily.\\
which concatenative synthesis projects can be developed easily.\\
Results demonstrate the wide variety of sounds that can be produced using
this method of synthesis. A number of technical issues are outlined that
@@ -92,7 +92,7 @@
\section*{Related Works}
A number of programs utilize CS to achieve various goals. The process has
been used for applications in areas such as Speech Synthesis, Instrument
been used for applications in areas such as speech synthesis, instrument
synthesis and for applications in creative sound design.\\
The wide range of applications demonstrates the versatility of this
synthesis technique. It differs from traditional synthesis methods through
@@ -186,8 +186,9 @@
Output is generated by analysing overlapping segments of audio (known as
grains) from both the target sound and the source database, then searching
for the closest matching grain in the source database to the target sound.
Finally, the output is generated by overlap-adding the best matches. Each
component will be discussed in detail in the following sections.\\
Finally, the output is generated by applying a hanning window and
overlap-adding the best matches. Each component will be discussed in detail
in the following sections.\\
When designing the concatenator framework, ease of development, use and
extensibility were primary considerations. It was for these reasons that
@@ -201,7 +202,7 @@
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 Python,
of prior CS research in Python and the author's previous experience,
made it the most suitable choice for this project.\\
The choice to limit the project to offline processing has both positive and
@@ -228,7 +229,7 @@
An additional consideration was the method to be used for controlling the
target to be matched too. It was decided that the most interesting results
would be produced through the matching of grains to a target audio file, as
opposed to other approaches such as matching to midi scores. In this sense
opposed to other approaches such as matching to MIDI scores. In this sense
the project is a form of offline audio-mosaicking tool similar to that of
CataRT.
@@ -276,7 +277,7 @@
\item Retrieving the best grain matches returned by the matching algorithm
\item Applying a window function
\item Overlapping the grains
\item Transform grains to match target
\item Transforming grains to match target
\item Saving the result to a file
\end{enumerate}
Initially, grains were not transformed to better match the target. This