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3 results on '"Note tracking"'

1. Escaping from the Abyss of Manual Annotation: New Methodology of Building Polyphonic Datasets for Automatic Music Transcription

Author

Su, Li, Yang, Yi-Hsuan, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Kronland-Martinet, Richard, editor, Aramaki, Mitsuko, editor, and Ystad, Sølvi, editor

Published
2016
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2. A supervised classification approach for note tracking in polyphonic piano transcription.

Author

Valero-Mas, Jose J., Benetos, Emmanouil, and Iñesta, José M.

Subjects
*PIANO music, *MECHANICAL musical instruments, *SUPERVISED learning, *TASK performance, *DATA analysis
Abstract

In the field of Automatic Music Transcription, note tracking systems constitute a key process in the overall success of the task as they compute the expected note-level abstraction out of a frame-based pitch activation representation. Despite its relevance, note tracking is most commonly performed using a set of hand-crafted rules adjusted in a manual fashion for the data at issue. In this regard, the present work introduces an approach based on machine learning, and more precisely supervised classification, that aims at automatically inferring such policies for the case of piano music. The idea is to segment each pitch band of a frame-based pitch activation into single instances which are subsequently classified as active or non-active note events. Results using a comprehensive set of supervised classification strategies on the MAPS piano data-set report its competitiveness against other commonly considered strategies for note tracking as well as an improvement of more than in terms of F-measure when compared to the baseline considered for both frame-level and note-level evaluations. [ABSTRACT FROM AUTHOR]

Published
2018
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3. A supervised classification approach for note tracking in polyphonic piano transcription

Author

Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos, Valero-Mas, Jose J., Benetos, Emmanouil, Iñesta, José M., Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos, Valero-Mas, Jose J., Benetos, Emmanouil, and Iñesta, José M.

Abstract

In the field of Automatic Music Transcription, note tracking systems constitute a key process in the overall success of the task as they compute the expected note-level abstraction out of a frame-based pitch activation representation. Despite its relevance, note tracking is most commonly performed using a set of hand-crafted rules adjusted in a manual fashion for the data at issue. In this regard, the present work introduces an approach based on machine learning, and more precisely supervised classification, that aims at automatically inferring such policies for the case of piano music. The idea is to segment each pitch band of a frame-based pitch activation into single instances which are subsequently classified as active or non-active note events. Results using a comprehensive set of supervised classification strategies on the MAPS piano data-set report its competitiveness against other commonly considered strategies for note tracking as well as an improvement of more than +10% in terms of F-measure when compared to the baseline considered for both frame-level and note-level evaluations.

Published
2018

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