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