A Parametric Piano Synthesizer

Rauhala, Jukka; Laurson, Mikael; Välimäki, Vesa; Lehtonen, Heidi-Maria; Norilo, Vesa

doi:10.1162/comj.2008.32.4.17

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…These systems can achieve realistic, interpretable, and controllable sound synthesis, but they require extensive modeling and precise measurements of physical elements [26]. For practical usages, such approaches can be efficiently implemented with digital waveguides [27] or modal synthesis [28].…”

Section: Related Workmentioning

confidence: 99%

DDSP-Piano: A Neural Sound Synthesizer Informed by Instrument Knowledge

Renault,

Mignot,

Roebel

2023

J. Audio Eng. Soc.

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Instrument sound synthesis using deep neural networks has received numerous improvements over the last couple of years. Among them, the Differentiable Digital Signal Processing (DDSP) framework has modernized the spectral modeling paradigm by including signal-based synthesizers and effects into fully differentiable architectures. The present work extends the applications of DDSP to the task of polyphonic sound synthesis, with the proposal of a differentiable piano synthesizer conditioned on MIDI inputs. The model architecture is motivated by high-level acoustic modeling knowledge of the instrument, which, along with the sound structure priors inherent to the DDSP components, makes for a lightweight, interpretable, and realistic-sounding piano model. A subjective listening test has revealed that the proposed approach achieves better sound quality than a state-of-the-art neural-based piano synthesizer, but physical-modeling-based models still hold the best quality. Leveraging its interpretability and modularity, a qualitative analysis of the model behavior was also conducted: it highlights where additional modeling knowledge and optimization procedures could be inserted in order to improve the synthesis quality and the manipulation of sound properties. Eventually, the proposed differentiable synthesizer can be further used with other deep learning models for alternative musical tasks handling polyphonic audio and symbolic data.

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Section: Related Workmentioning

confidence: 99%

DDSP-Piano: A Neural Sound Synthesizer Informed by Instrument Knowledge

Renault,

Mignot,

Roebel

2023

J. Audio Eng. Soc.

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“…See, e.g., [82,83]. Digital waveguides have been employed for synthesis for a variety of stringed instruments beyond bowed strings [84] (their first use [79]), including the guitar [51,85,86], harpsichord [87], piano [32,88], the Clavinet [89] and the Finnish kantele [71] among many others.…”

Section: Digital Waveguidesmentioning

confidence: 99%

Models of musical string vibration

Bilbao

Ducceschi

2023

Acoust. Sci. & Tech.

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Musical string vibration has been the subject of scientific study for centuries. Recent increases in computational power have allowed the exploration of increasingly detailed features of perceptual significance through simulation approaches. The starting point for any simulation is a welldefined model, usually framed as a system of differential equations, with parameters determined by measurement and experiment. This review article is intended to take the reader through models of string vibration progressively, beginning with well-known and well-studied linear models, and then introducing new features that form the basis for the modern study of realistic musical string vibration. These include, first, nonlinear excitation mechanisms, such as the hammer-string and bow-string interaction, and then the collision mechanism, both for pointwise obstructions and over a distributed region. Finally, the linear model of string vibration is generalized to include geometric nonlinear effects, leading to typical nonlinear behaviour such as pitch glides and the appearance of so-called phantom partials due to nonlinear mixing of modes. The article concludes with a general overview of numerical simulation techniques for string vibration.

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“…Partial decay time analysis from Clavinet tones reveals ripply T 60 also shown by microphone-recorded tones. This can be easily reproduced by the use of a so-called ripple filter, which has been used for the emulation of other instruments as well, such as the harpsichord [41] and the piano [44].…”

Section: String Modelmentioning

confidence: 99%

A digital waveguide-based approach for Clavinet modeling and synthesis

Gabrielli

Välimäki

Penttinen

et al. 2013

EURASIP J. Adv. Signal Process.

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The Clavinet is an electromechanical musical instrument produced in the mid-twentieth century. As is the case for other vintage instruments, it is subject to aging and requires great effort to be maintained or restored. This paper reports analyses conducted on a Hohner Clavinet D6 and proposes a computational model to faithfully reproduce the Clavinet sound in real time, from tone generation to the emulation of the electronic components. The string excitation signal model is physically inspired and represents a cheap solution in terms of both computational resources and especially memory requirements (compared, e.g., to sample playback systems). Pickups and amplifier models have been implemented which enhance the natural character of the sound with respect to previous work. A model has been implemented on a real-time software platform, Pure Data, capable of a 10-voice polyphony with low latency on an embedded device. Finally, subjective listening tests conducted using the current model are compared to previous tests showing slightly improved results.

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A Parametric Piano Synthesizer

Cited by 8 publications

References 22 publications

DDSP-Piano: A Neural Sound Synthesizer Informed by Instrument Knowledge

DDSP-Piano: A Neural Sound Synthesizer Informed by Instrument Knowledge

Models of musical string vibration

A digital waveguide-based approach for Clavinet modeling and synthesis

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