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In this paper, a method for automatic transcription of polyphonic music is proposed that exploits key information. The proposed system performs key detection using a matching technique with distributions of pitch class pairs, called Zweiklang profiles. The automatic transcription system is based on probabilistic latent component analysis, supporting templates from multiple instruments, as well as tuning deviations and frequency modulations. Key information is incorporated to the transcription system using Dirichlet priors during the parameter update stage. Experiments are performed on a polyphonic, multiple-instrument dataset of Bach chorales, where it is shown that incorporating key information improves multi-pitch detection and instrument assignment performance.
Author (s): Benetos, Emmanouil; Jansson, Andreas; Weyde, Tillman
Affiliation:
City University London, London, UK
(See document for exact affiliation information.)
Publication Date:
2014-01-06
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Session subject:
Automatic Music Transcription
Permalink: https://aes2.org/publications/elibrary-page/?id=17103
(195KB)
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Benetos, Emmanouil; Jansson, Andreas; Weyde, Tillman; 2014; Improving Automatic Music Transcription Through Key Detection [PDF]; City University London, London, UK; Paper 3-2; Available from: https://aes2.org/publications/elibrary-page/?id=17103
Benetos, Emmanouil; Jansson, Andreas; Weyde, Tillman; Improving Automatic Music Transcription Through Key Detection [PDF]; City University London, London, UK; Paper 3-2; 2014 Available: https://aes2.org/publications/elibrary-page/?id=17103