Abstract
Cross entropy, a measurement of the complexity/predictability of a series of observations given a probabilistic model, has been used in a variety of domains in music scholarship for decades. This paper presents a novel application of this metric to musical corpus analysis. Given a series of divisions to a larger corpus, a sub-corpus is relatively “unique” if a probabilistic model derived from its pieces better predicts its constituent pieces than do models derived from other sub-corpora. A sub-corpus is relatively “coherent” if its own model describes its pieces better than a model derived from the entire corpus. The Yale-Classical-Archives corpus was used to illustrate several strategies for sub-corpus division, each of which are tested for uniqueness and coherence. Some broader interpretive applications are also described.
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White, C.W. (2017). Cross Entropy as a Measure of Coherence and Uniqueness. In: Agustín-Aquino, O., Lluis-Puebla, E., Montiel, M. (eds) Mathematics and Computation in Music. MCM 2017. Lecture Notes in Computer Science(), vol 10527. Springer, Cham. https://doi.org/10.1007/978-3-319-71827-9_25
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DOI: https://doi.org/10.1007/978-3-319-71827-9_25
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