11,203 research outputs found

    A machine learning approach to voice separation in lute tablature

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    Beat histogram features from NMF-based novelty functions for music classification

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    In this paper we present novel rhythm features derived from drum tracks extracted from polyphonic music and evaluate them in a genre classification task. Musical excerpts are analyzed using an optimized, partially fixed Non-Negative Matrix Factorization (NMF) method and beat histogram features are calculated on basis of the resulting activation functions for each one out of three drum tracks extracted (Hi-Hat, Snare Drum and Bass Drum). The features are evaluated on two widely used genre datasets (GTZAN and Ballroom) using standard classification methods, concerning the achieved overall classification accuracy. Furthermore, their suitability in distinguishing between rhythmically similar genres and the performance of the features resulting from individual activation functions is discussed. Results show that the presented NMF-based beat histogram features can provide comparable performance to other classification systems, while considering strictly drum patterns

    Using discovered, polyphonic patterns to filter computer-generated music

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    A metric for evaluating the creativity of a music-generating system is presented, the objective being to generate mazurka-style music that inherits salient patterns from an original excerpt by Frédéric Chopin. The metric acts as a filter within our overall system, causing rejection of generated passages that do not inherit salient patterns, until a generated passage survives. Over fifty iterations, the mean number of generations required until survival was 12.7, with standard deviation 13.2. In the interests of clarity and replicability, the system is described with reference to specific excerpts of music. Four concepts–Markov modelling for generation, pattern discovery, pattern quantification, and statistical testing–are presented quite distinctly, so that the reader might adopt (or ignore) each concept as they wish
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