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The Sousta corpus: Beat-informed automatic transcription of traditional dance tunes
Austrian Research Institute for Artificial Intelligence (OFAI).ORCID iD: 0000-0003-1679-6018
2016 (English)In: Proceedings of ISMIR - International Conference on Music Information Retrieval, 2016, 531-537 p.Conference paper (Refereed)
Abstract [en]

In this paper, we present a new corpus for research in computational ethnomusicology and automatic music transcription, consisting of traditional dance tunes from Crete. This rich dataset includes audio recordings, scores transcribed by ethnomusicologists and aligned to the audio performances, and meter annotations. A second contribution of this paper is the creation of an automatic music transcription system able to support the detection of multiple pitches produced by lyra (a bowed string instrument). Furthermore, the transcription system is able to cope with deviations from standard tuning, and provides temporally quantized notes by combining the output of the multi-pitch detection stage with a state-of-the-art meter tracking algorithm. Experiments carried out for note tracking using 25ms onset tolerance reach 41.1% using information from the multi-pitch detection stage only, 54.6% when integrating beat information, and 57.9% when also supporting tuning estimation. The produced meter aligned transcriptions can be used to generate staff notation, a fact that increases the value of the system for studies in ethnomusicologyIn this paper, we present a new corpus for research in computational ethnomusicology and automatic music transcription, consisting of traditional dance tunes from Crete. This rich dataset includes audio recordings, scores transcribed by ethnomusicologists and aligned to the audio performances, and meter annotations. A second contribution of this paper is the creation of an automatic music transcription system able to support the detection of multiple pitches produced by lyra (a bowed string instrument). Furthermore, the transcription system is able to cope with deviations from standard tuning, and provides temporally quantized notes by combining the output of the multi-pitch detection stage with a state-of-the-art meter tracking algorithm. Experiments carried out for note tracking using 25ms onset tolerance reach 41.1% using information from the multi-pitch detection stage only, 54.6% when integrating beat information, and 57.9% when also supporting tuning estimation. The produced meter aligned transcriptions can be used to generate staff notation, a fact that increases the value of the system for studies in ethnomusicology

Place, publisher, year, edition, pages
2016. 531-537 p.
Keyword [en]
Cretan music, Transcription, Beat tracking
National Category
Media Engineering
Research subject
Computer Science; Media Technology; Speech and Music Communication
Identifiers
URN: urn:nbn:se:kth:diva-193751OAI: oai:DiVA.org:kth-193751DiVA: diva2:1040419
Conference
ISMIR - International Conference on Music Information Retrieval
Note

QC 20161031

Available from: 2016-10-27 Created: 2016-10-10 Last updated: 2016-11-11Bibliographically approved

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Holzapfel, André
Media Engineering

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