The internet has facilitated music metadata production and distribution on an unprecedented scale. A contributing factor of this data deluge is a change in the authorship of this data from the expert few to the untrained crowd. The resulting unordered flood of imperfect annotations provides challenges and opportunities in identifying accurate metadata and linking it to the music audio in order to provide a richer listening experience. We advocate novel adaptations of Dynamic Programming for music metadata synchronisation, ranking and comparison. This thesis introduces Windowed Time Warping, Greedy, Constrained On-Line Time Warping for synchronisation and the Concurrence Factor for automatically ranking metadata. We begin by examining the availability of various music metadata on the web. We then review Dynamic Programming methods for aligning and comparing two source sequences whilst presenting novel, specialised adaptations for efficient, realtime synchronisation of music and metadata that make improvements in speed and accuracy over existing algorithms. The Concurrence Factor, which measures the degree in which an annotation of a song agrees with its peers, is proposed in order to utilise the wisdom of the crowds to establish a ranking system. This attribute uses a combination of the standard Dynamic Programming methods Levenshtein Edit Distance, Dynamic Time Warping, and Longest Common Subsequence to compare annotations. We present a synchronisation application for applying the aforementioned methods as well as a tablature-parsing application for mining and analysing guitar tablatures from the web. We evaluate the Concurrence Factor as a ranking system on a largescale collection of guitar tablatures and lyrics to show a correlation with accuracy that is superior to existing methods currently used in internet search engines, which are based on popularity and human ratings.

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