Automatic System for the Arrangement of Piano Reductions

Chiu, Shih-Chuan; Shan, Man-Kwan; Huang, Jia-Hong

doi:10.1109/ism.2009.105

Cited by 12 publications

(8 citation statements)

References 3 publications

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“…In addition to voice separation, prior work has explored automatic music arrangement. The primary focus of prior work for automatic music arrangement has been on reduction-mapping musical scores for large ensembles to parts playable by a single specific instrument such as the piano [12][13][14][15][16][17], guitar [18][19][20] or bass [21]. This past work focuses on identifying the least important notes to delete so that the resultant score is playable on a single instrument, whereas our work seeks to preserve the original score in its entirety and satisfy playability for multiple instruments simultaneously.…”

Section: Prior Workmentioning

confidence: 99%

Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music

Dong¹,

Donahue²,

Berg-Kirkpatrick³

et al. 2021

Preprint

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Modern keyboards allow a musician to play multiple instruments at the same time by assigning zones-fixed pitch ranges of the keyboard-to different instruments. In this paper, we aim to further extend this idea and examine the feasibility of automatic instrumentation-dynamically assigning instruments to notes in solo music during performance. In addition to the online, real-time-capable setting for performative use cases, automatic instrumentation can also find applications in assistive composing tools in an offline setting. Due to the lack of paired data of original solo music and their full arrangements, we approach automatic instrumentation by learning to separate parts (e.g., voices, instruments and tracks) from their mixture in symbolic multitrack music, assuming that the mixture is to be played on a keyboard. We frame the task of part separation as a sequential multi-class classification problem and adopt machine learning to map sequences of notes into sequences of part labels. To examine the effectiveness of our proposed models, we conduct a comprehensive empirical evaluation over four diverse datasets of different genres and ensembles-Bach chorales, string quartets, game music and pop music. Our experiments show that the proposed models outperform various baselines. We also demonstrate the potential for our proposed models to produce alternative convincing instrumentations for an existing arrangement by separating its mixture into parts. All source code and audio samples can be found at https://salu133445.github.io/arranger/.

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Section: Prior Workmentioning

confidence: 99%

Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music

Dong¹,

Donahue²,

Berg-Kirkpatrick³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…, 5 = little finger. 1 According to the model (Fig. 2), a fingering motion f 1:N = ( f n ) N n=1 is first generated by an initial probability P ( f 1 ) and transition probabilities P ( f n | f n−1 ).…”

Section: I Q U a N T I T A T I V E M E A S U R E S O F P E R F O mentioning

confidence: 99%

“…Arranging a musical piece to change difficulty, for example, to make it playable for beginners, is also widely practiced. To automate these processes, systems for piano arrangement [1–5], guitar arrangement [6–8], and orchestration [9,10] have been studied. This study aims at a system for piano reduction, i.e.…”

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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“…Arranging a musical piece to change difficulty, for example, to make it playable for beginners, is also widely practiced. To automate these processes, systems for piano arrangement [1][2][3][4][5], guitar arrangement [6][7][8], and orchestration [9,10] have been studied. This study aims at a system for piano reduction, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…However, these conditions cannot be thought of as necessary nor sufficient conditions for playable scores because in reality there can be a piano score with chords with more than 5 notes and/or spanning a large pitch interval that are conventionally played as broken chords, and even scores without chords (melodies) can be unplayable in fast tempos. In fact, it is difficult to find a complete description of playable scores that is valid in every situation because the condition depends on player's skill and 1 Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan can change continuously with the tempo. 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.…”

Section: Introductionmentioning

confidence: 99%

Statistical Piano Reduction Controlling Performance Difficulty

Nakamura¹,

Yoshii²

2018

Preprint

View full text Add to dashboard Cite

We present a statistical-modelling 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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Automatic System for the Arrangement of Piano Reductions

Cited by 12 publications

References 3 publications

Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music

Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music

Statistical piano reduction controlling performance difficulty

Statistical Piano Reduction Controlling Performance Difficulty

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