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\chapter{Initial Results}  \section{Smith-Waterman Results}  \subsection{Normalization}  \item Problem, just given a raw score independent of song length or anything else  \item Attempt to solve: $normalization$ $equation$ $used$  \subsection{Clustering}  \item Tried out using graph clustering algorithm  \item Future: try k-means  \section{Visualization}  \subsection{Motivation}  \item Given over 100 pairwise (triangle) number comparisons between sets of songs, it is important to have a useful means of visualizing the data  \item Initially tried using Graphos and Circos and other graphing libraries  \item Demanded a level of interactivity unachievable with other programs  \subsection{Using D3.js}  \item Using popular javascript graphing library, can construct a visualization based on hierarchical edge bundling, as used in other modern graphing libraries (curved edges using tension factors)  \item Can add interactivity using web server  \subsection{Using Twistd webserver}  \item Facile, easy use web server all in Python. Link C libraries to Python and have a rapid coding environment that has fast native C-code at its base  \item Can compare align results in a visual interface based on reconstructing the aligned data  \subsection{Audio playback}  \item Audio transposition problem, and solution using fewest localized transpositions  \item Following along using popcorn.js  \subsection{Public implementation}  \item Webserver available at chordmatch.com