Titlebook: Computational Phonogram Archiving; Rolf Bader Book 2019 Springer Nature Switzerland AG 2019 phonographical archiving.computational phonogr

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ETCH

Book 2019cal features like timbre, melody, rhythm or musical form, and artificial intelligence then sorts and relates these features. At the first International Symposium on Computational Ethnomusicological Archiving held on November 9 to 11, 2017 at the Institute of Systematic Musicology in Hamburg, Germany

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Computational Music Archiving as Physical Culture Theory the world with computational tools. Its analysis is based on Music Information Retrieval (MIR) tools, the sorting algorithms used are Hidden-Markov models and self-organazing Kohonen maps (SOM). Different kinds of systematizations like taxonomies, self-organazing systems as well as bottom-up method

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Preamble

Temperament in Tuning Systems of Southeast Asia and Ancient Indiase cultures, the literature as well as oral tradition on theoretical reflections of why choosing which tuning system are scarce. For Western scholars, only considering the pitches played or the tuning of instruments is not perfectly satisfying, as it lacks insight into the reasons for certain tuning

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John Blacking Revisited—Comparative Analysis of Venda Tshikone Dance (1958 and 2009)arts and elements in folk tunes are sensitive to change and what are the stable elements of the melodies. Researchers had data collected over several decades, which gave a great opportunity to systematically explore stability of melodies over the years and decades.

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RACE

Analysis and Perception of Javanese , Tuningsy the tunings of . ensembles have fascinated researchers since the early stages of the disciplines of Systematic and Comparative Musicology, as these tunings are considerably different from Western, Middle Eastern or other music traditions and even differ between . ensembles. Additionally most instr

Brocas-Area

Content-Based Music Retrieval and Visualization System for Ethnomusicological Music Archivessimilarity. The system extracts an onsets-synchronous timbre feature of each audio file of a given collection. From the resulting time series, Hidden Markov Model are trained. The transition probability matrices of the models are considered a rhythm fingerprint that represents the musics rhythmic st

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