![]() In test, we align these sequences to the audio, accounting for changes in key, different interpretations, and missing structural information. This is investigated through the use of guitar chord sequences obtained from the web. Our system is also able to learn from partially-labelled data. When sufficient training examples are available, we find that our model achieves similar performance on both the well-known and novel datasets and statistically significantly outperforms a baseline Hidden Markov Model. In the months prior to the completion of this thesis, a large number of new, fully-labelled datasets have been released to the research community, meaning that the generalisation potential of models may be tested. This performance is realised by the introduction of a novel Dynamic Bayesian Net- work and chromagram feature vector, which concurrently recognises chords, keys and bass note sequences on a set of songs by The Beatles, Queen and Zweieck. In this thesis we introduce a machine learning based automatic chord recognition algorithm that achieves state of the art performance.
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