Gerhard Widmer - Academia.edu (original) (raw)
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Papers by Gerhard Widmer
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ABSTRACT
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Journal of Machine Learning Research, Oct 1, 2012
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IOS PressPUB827Amsterdam, The Netherlands, The Netherlands, Aug 1, 2001
This article presents a long‐term inter‐disciplinary research project situated ... more This article presents a long‐term inter‐disciplinary research project situated at the intersection of the scientific disciplines of Musicology and Artificial Intelligence. The goal is to develop AI, and in particular machine learning and data mining, methods to study the ...
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Recent advances in real-time music score following have made it possible for machines to automati... more Recent advances in real-time music score following have made it possible for machines to automatically track highly complex polyphonic music, including full orchestra performances. In this paper, we attempt to take this to an even higher level, namely, live tracking of full operas. We first apply a state-of-the-art audio alignment method based on online Dynamic Time-Warping (OLTW) to full-length recordings of a Mozart opera and, analyzing the tracker's most severe errors, identify three common sources of problems specific to the opera scenario. To address these, we propose a combination of a DTW-based music tracker with specialized audio event detectors (for applause, silence/noise, and speech) that condition the DTW algorithm in a topdown fashion, and show, step by step, how these detectors add robustness to the score follower. However, there remain a number of open problems which we identify as targets for ongoing and future research.
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Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 2014
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ABSTRACT
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Journal of Machine Learning Research, Oct 1, 2012
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IOS PressPUB827Amsterdam, The Netherlands, The Netherlands, Aug 1, 2001
This article presents a long‐term inter‐disciplinary research project situated ... more This article presents a long‐term inter‐disciplinary research project situated at the intersection of the scientific disciplines of Musicology and Artificial Intelligence. The goal is to develop AI, and in particular machine learning and data mining, methods to study the ...
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Recent advances in real-time music score following have made it possible for machines to automati... more Recent advances in real-time music score following have made it possible for machines to automatically track highly complex polyphonic music, including full orchestra performances. In this paper, we attempt to take this to an even higher level, namely, live tracking of full operas. We first apply a state-of-the-art audio alignment method based on online Dynamic Time-Warping (OLTW) to full-length recordings of a Mozart opera and, analyzing the tracker's most severe errors, identify three common sources of problems specific to the opera scenario. To address these, we propose a combination of a DTW-based music tracker with specialized audio event detectors (for applause, silence/noise, and speech) that condition the DTW algorithm in a topdown fashion, and show, step by step, how these detectors add robustness to the score follower. However, there remain a number of open problems which we identify as targets for ongoing and future research.
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Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 2014
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