Analysis of analysis: importance of different musical parameters for Schenkerian analysis

While criteria for Schenkerian analysis have been much discussed, such discussions have generally not been informed by data. Kirlin [Kirlin, Phillip B., 2014 “A Probabilistic Model of Hierarchical Music Analysis.” Ph.D. thesis, University of Massachusetts Amherst] has begun to fill this vacuum with a corpus of textbook Schenkerian analyses encoded using data structures suggested byYust [Yust, Jason, 2006 “Formal Models of Prolongation.” Ph.D. thesis, University of Washington] and a machine learning algorithm based on this dataset that can produce analyses with a reasonable degree of accuracy. In this work, we examine what musical features (scale degree, harmony, metrical weight) are most significant in the performance of Kirlin's algorithm.Accepted manuscrip

Multileveled rhythmic structure of ragtime

Syncopation in ragtime music has been defined in multiple ways. In this study we propose a method using the Hadamard transform. We extract four-measure phrases from a corpus of ragtime pieces by Scott Joplin, James Scott, and Joseph Lamb, and convert them to 32-element binary onset vectors. The Hadamard transform converts this to another 32-element vector that can be interpreted as representing syncopation at various metrical levels. This method is closely related to a similar application of the discrete Fourier transform. Using the Hadamard representation, we show that syncopation is strongest at the quarter-note level, and that tresillo-like rhythms are especially characteristic of the genre. We identify a number of significant differences based on the position of a phrase in a sixteen-measure strain, the position of the strain in the rag, and the composer. The Hadamard representation also facilitates discovery of relationship between different levels of rhythmic organization.Accepted manuscrip

VoiSe: Learning to Segregate Voices in Explicit and Implicit Polyphony

Finding multiple occurrences of themes and patterns in music can be hampered due to polyphonic textures. This is caused by the complexity of music that weaves multiple independent lines of music together. We present and demonstrate a system, VoiSe, that is capable of isolating individual voices in both explicit and implicit polyphonic music. VoiSe is designed to work on a symbolic representation of a music score, and consists of two components: a same-voice predicate implemented as a learned decision tree, and a hard-coded voice numbering algorithm