At a time when fundamentalism is on the rise, traditional religions are in decline and postmodernity has challenged any system that claims to be all-defining, young people have left their traditional places of worship and set up their own, in clubs, at festivals and within music culture. This book explores the development of a range of cults of popular music as a response to changes in attitudes to meaning, spirituality and religion in society. These data are exploited using curriculum learning, where we see an improvement from when testing on a set of 715 songs and evaluated on a complex chord alphabet. Here we align over 1, 000 chord sequences to audio and use them as an additional training source. Another use for these sequences is in a training scenario. We find that this approach increases recognition accuracy from on a set of songs by the rock group The Beatles. 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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