Note: None of the following is fictional or exaggerated. These are all things that I have personally encountered in little more than a year at a publishing house. Letters, emails, phone calls, strange parcels, some mild stalking. Queries full of bad grammar and spelling come in all the time, but these are some that truly stand out.
She attended Enfield County School, an all-girls comprehensive school, before moving to the Latymer School, a selective grammar school for sixth form education.[5] She has undergraduate and master's degrees in English literature from University College London.[7]
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In this thesis I argue that in order for machines to perform musical tasks like humans do, in particular to predict music, they need to be subjected to a similar enculturation process by design. This work is grounded in an information theoretic framework that takes cultural context into account. I introduce a measure of musical entropy to analyze the predictability of musical events as a function of prior musical exposure. Then I discuss computational models for music representation that are informed by genre-specific containers for musical elements like notes. Finally I propose a software framework for automatic music prediction. The system extracts a lexicon of melodic, or timbral, and rhythmic primitives from audio, and generates a hierarchical grammar to represent the structure of a particular musical form. To improve prediction accuracy, context can be switched with cultural plug-ins that are designed for specific musical instruments and genres. be457b7860
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