Score-informed transcription for automatic piano tutoring. Benetos, E., Klapuri, A., & Dixon, S. In European Signal Processing Conference, pages 2153–2157, 2012. IEEE. Journal Abbreviation: European Signal Processing Conference
Score-informed transcription for automatic piano tutoring [link]Paper  abstract   bibtex   
In this paper, a score-informed transcription method for auto- matic piano tutoring is proposed. The method takes as input a recording made by a student which may contain mistakes, along with a reference score. The recording and the aligned synthesized score are automatically transcribed using the non-negative matrix factorization algorithm for multi-pitch estimation and hidden Markov models for note tracking. By comparing the two transcribed recordings, common errors occurring in transcription algorithms such as extra octave notes can be suppressed. The result is a piano-roll descrip- tion which shows the mistakes made by the student along with the correctly played notes. Evaluation was performed on six pieces recorded using a Disklavier piano, using both manually-aligned and automatically-aligned scores as an in- put. Results comparing the system output with ground-truth annotation of the original recording reach a weighted F- measure of 93%, indicating that the proposed method can successfully analyze the student's performance.
@inproceedings{benetos_score-informed_2012,
	series = {European {Signal} {Processing} {Conference}},
	title = {Score-informed transcription for automatic piano tutoring},
	url = {https://ieeexplore.ieee.org/document/6334095},
	abstract = {In this paper, a score-informed transcription method for auto- matic piano tutoring is proposed. The method takes as input a recording made by a student which may contain mistakes, along with a reference score. The recording and the aligned synthesized score are automatically transcribed using the non-negative matrix factorization algorithm for multi-pitch estimation and hidden Markov models for note tracking. By comparing the two transcribed recordings, common errors occurring in transcription algorithms such as extra octave notes can be suppressed. The result is a piano-roll descrip- tion which shows the mistakes made by the student along with the correctly played notes. Evaluation was performed on six pieces recorded using a Disklavier piano, using both manually-aligned and automatically-aligned scores as an in- put. Results comparing the system output with ground-truth annotation of the original recording reach a weighted F- measure of 93\%, indicating that the proposed method can successfully analyze the student's performance.},
	booktitle = {European {Signal} {Processing} {Conference}},
	publisher = {IEEE},
	author = {Benetos, Emmanouil and Klapuri, Anssi and Dixon, Simon},
	year = {2012},
	note = {Journal Abbreviation: European Signal Processing Conference},
	keywords = {\#nosource, ⛔ No DOI found},
	pages = {2153--2157},
}

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