Score Analyzer: Extracting performance information for instrumental e-learning

Sébastien, Véronique; Conruyt, Noël; Sébastien, Olivier

doi:10.1109/icl.2012.6402061

Cited by 2 publications

(5 citation statements)

References 5 publications

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Section: Relation With Previous Workmentioning

confidence: 99%

“…In this section we refer to the main methods to model difficulty in piano repertoire [8,5,11]. Sebastien et al [8] propose a list of different instrument-agnostic descriptors for difficulty classification. The list of descriptors was further extended by Chen et al [5] proposing different feature spaces for measuring difficulty in the piano repertoire.…”

Section: Relation With Previous Workmentioning

confidence: 99%

“…Certain instrument forums and websites such as 8note also release difficulty grades in different ranges, generally three or four grades from beginner to advanced. When it comes to the difficulty datasets in MIR, both previous datasets [8,5] are not publicly available.…”

Section: Introductionmentioning

confidence: 99%

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Score Difficulty Analysis for Piano Performance Education based on Fingering

Ramoneda

Tamer

Eremenko

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we introduce score difficulty classification as a subtask of music information retrieval (MIR), which may be used in music education technologies, for personalised curriculum generation, and score retrieval. We introduce a novel dataset for our task, Mikrokosmos-difficulty, containing 147 piano pieces in symbolic representation and the corresponding difficulty labels derived by its composer Béla Bartók and the publishers. As part of our methodology, we propose piano technique feature representations based on different piano fingering algorithms. We use these features as input for two classifiers: a Gated Recurrent Unit neural network (GRU) with attention mechanism and gradient-boosted trees trained on score segments. We show that for our dataset fingering based features perform better than a simple baseline considering solely the notes in the score. Furthermore, the GRU with attention mechanism classifier surpasses the gradient-boosted trees. Our proposed models are interpretable and are capable of generating difficulty feedback both locally, on short term segments, and globally, for whole pieces. Code, datasets, models, and an online demo are made available for reproducibility.

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Section: Relation With Previous Workmentioning

confidence: 99%

Section: Relation With Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Score Difficulty Analysis for Piano Performance Education based on Fingering

Ramoneda

Tamer

Eremenko

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

show abstract

“…In previous studies [11,12], features such as playing speed, note density, pitch entropy, hand displacement rate, hand stretch, and fingering complexity have been considered to estimate the difficulty level of piano scores. These features are incorporated in the above difficulty measures, although in an implicit manner.…”

Section: Quantitative Measures Of Performance Difficultymentioning

confidence: 99%

“…A possible solution is to quantify performance difficulty and use it as an indicator of playable scores in each situation of skill level, tempo, etc. [11,12].…”

Section: Introductionmentioning

confidence: 99%

Statistical piano reduction controlling performance difficulty

Nakamura

Yoshii

2018

SIP

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We present a statistical-modeling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.

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Score Analyzer: Extracting performance information for instrumental e-learning

Cited by 2 publications

References 5 publications

Score Difficulty Analysis for Piano Performance Education based on Fingering

Score Difficulty Analysis for Piano Performance Education based on Fingering

Statistical piano reduction controlling performance difficulty

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