Imitative Computer-Aided Musical Orchestration with Biologically Inspired Algorithms

Caetano, Marcelo; Cella, Carmine Emanuele

doi:10.1007/978-3-030-72116-9_20

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…This idea stems from the musical works of spectral composers, such as Gérard Grisey and Tristan Murail, with a software driven approach to orchestration originally being proposed back in the early 2000s by the composer and orchestration specialist Yan Maresz [1]. Throughout the development of numerous target-based orchestration systems, the predominant architectural approach has been to use a genetic algorithm [23][24][25], a methodology which is also used in the recent software orchestration tool Orchidea [26,27]. A genetic algorithm is here used as a heuristic approach to solving a knapsack-like problem [28], asserting that the problem space be discretised and interpreted as a classification based problem.…”

Section: Technological Backgroundmentioning

confidence: 99%

Towards Orchestrating Physically Modelled 2D Percussion Instruments

Wolstanholme,

McPherson

2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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Contemporary orchestration practice harbours a number of aesthetic inquiries relating to the employment and arrangement of percussion instruments. Due in part to the fact that percussion instruments largely occupy an inharmonic timbre space, they encompass a diverse and distinctly nuanced musical idiom in comparison to harmonic instruments, both in terms of their textural interplay, musical function and cultural significance. In response to this perspective, we present a neural network approach to the parameter estimation of physically modelled, abstract percussion instruments. The approach presented here serves as our initial attempt towards creating a computer-assisted orchestration methodology specifically targeting the musical employment and arrangement of inharmonic timbres and percussive instruments. The neural architecture presented here has been trained and tested using a pair of two-dimensional physical models, to gauge a sense of our architecture's successes and limitations as we continue to expand this approach to include more two-dimensional models. This works poses as our first technological inquiry into this field, which has here been quantitatively assessed, with plans to undertake more rigorous and comparative tests in the near future.

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Section: Technological Backgroundmentioning

confidence: 99%

Towards Orchestrating Physically Modelled 2D Percussion Instruments

Wolstanholme,

McPherson

2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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“…The few samples with error above 80 cents were flagged and inspected manually on an individual basis (see section 3.6). The resampling was done via the Python resampy module, with its kaiser best filter 3 . The fundamental frequency was estimated via Essentia's PitchYin descriptor [9], and verified by ear for every resampled sound.…”

Section: Retuningmentioning

confidence: 99%

“…In this case, the orchestration solutions would work poorly in real-life orchestral scenarios. Among the state-of-the-art systems for computer-aided orchestration there is the Orch* family: Orchidée (2008) [2], Orchids (2013) [3], Orchidea (2017) [4], developed at…”

Section: Introductionmentioning

confidence: 99%

OrchideaSOL: a dataset of extended instrumental techniques for computer-aided orchestration

Cella,

Ghisi,

Lostanlen

et al. 2020

Preprint

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This paper introduces OrchideaSOL, a free dataset of samples of extended instrumental playing techniques, designed to be used as default dataset for the Orchidea framework for target-based computer-aided orchestration.OrchideaSOL is a reduced and modified subset of Studio On Line, or SOL for short, a dataset developed at Ircam between 1996 and 1998. We motivate the reasons behind OrchideaSOL and describe the differences between the original SOL and our dataset. We will also show the work done in improving the dynamic ranges of orchestral families and other aspects of the data.

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Orchidea: a comprehensive framework for target-based computer-assisted dynamic orchestration

Cella

2022

Journal of New Music Research

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Imitative Computer-Aided Musical Orchestration with Biologically Inspired Algorithms

Cited by 3 publications

References 53 publications

Towards Orchestrating Physically Modelled 2D Percussion Instruments

Towards Orchestrating Physically Modelled 2D Percussion Instruments

OrchideaSOL: a dataset of extended instrumental techniques for computer-aided orchestration

Orchidea: a comprehensive framework for target-based computer-assisted dynamic orchestration

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