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The concept of form in music encompasses a wide range of musical aspects, such as phrases and (hierarchical) segmentation, formal functions, cadences and voice-leading schemata, form templates, and repetition structure. In an effort towards a unified model of form, this paper proposes an integration of repetition structure (i.e., which segments of a piece occur several times) and formal templates (such as AABA). While repetition structure can be modeled using context-free grammars, most prior approaches allow for arbitrary grammar rules. Constraining the structure of the inferred rules to conform to a small set of templates (meta-rules) not only reduces the space of possible rules that need to be considered but also ensures that the resulting repetition grammar remains interpretable in the context of musical form. The resulting formalism can be extended to cases of varied repetition and thus constitutes a building block for a larger model of form. [ABSTRACT FROM AUTHOR]

Musicians spend countless hours practicing their instruments. To document and organize this time, musicians commonly use practice charts to log their practice. However, manual techniques require time, dedication, and experience to master, are prone to fallacy and omission, and ultimately can not describe the subtle variations in each repetition. This paper presents an alternative: by analyzing and classifying the audio recorded while practicing, logging could occur automatically, with levels of detail, accuracy, and ease that would not be possible otherwise. Towards this goal, we introduce the problem of Automatic Practice Logging (APL), including a discussion of the benefits and unique challenges it raises. We then describe a new dataset of over 600 annotated recordings of solo piano practice, which can be used to design and evaluate APL systems. After framing our approach to the problem, we present an algorithm designed to align short segments of practice audio with reference recordings using pitch chroma and dynamic time warping. [ABSTRACT FROM AUTHOR]

Computational music structure analysis encompasses any model attempting to organize music into qualitatively salient structural units, which can include anything in the heirarchy of large scale form, down to individual phrases and notes. While much existing audio-based segmentation work attempts to capture repetition and homogeneity cues useful at the form and thematic level, the time scales involved in phrase-level segmenation and the avoidance of repetition in improvised music necessitate alternate approaches in approaching jazz structure analysis. Recently, the Weimar Jazz Database has provided transcriptions of solos by a variety of eminent jazz performers. Utilizing a subset of these transcriptions aligned to their associated audio sources, we propose a model based on supervised training of a Hidden Markov Model with ground-truth state sequences designed to encode melodic contours appearing frequently in jazz improvisations. Results indicate that representing likely melodic contours in this way allows a lowlevel audio feature set containing primarily timbral and harmonic information to more accurately predict phrase boundaries. [ABSTRACT FROM AUTHOR]