Physically Informed Signal Processing Methods for Piano Sound Synthesis: A Research Overview

Bank, Balázs; Avanzini, Federico; Borin, Gianpaolo; Poli, Giovanni De; Fontana, Federico; Rocchesso, Davide

doi:10.1155/s1110865703304093

Cited by 25 publications

(29 citation statements)

References 28 publications

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“…With regard to the specific case of the piano, one can mention, for example, the very popular method of digital waveguides, through which a piano model was proposed by [25] and reviewed by [4]. This method is particularly effective and efficient and can be coupled with more traditional methods (as for instance finite differences in [5]).…”

Section: Introductionmentioning

confidence: 99%

Time domain simulation of a piano. Part 1: model description

2014

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Abstract. The purpose of this study is the time domain modeling of a piano. We aim at explaining the vibratory and acoustical behavior of the piano, by taking into account the main elements that contribute to sound production. The soundboard is modeled as a bidimensional thick, orthotropic, heterogeneous, frequency dependent damped plate, using Reissner Mindlin equations. The vibroacoustics equations allow the soundboard to radiate into the surrounding air, in which we wish to compute the complete acoustical field around the perfectly rigid rim. The soundboard is also coupled to the strings at the bridge, where they form a slight angle from the horizontal plane. Each string is modeled by a one dimensional damped system of equations, taking into account not only the transversal waves excited by the hammer, but also the stiffness thanks to shear waves, as well as the longitudinal waves arising from geometric nonlinearities. The hammer is given an initial velocity that projects it towards a choir of strings, before being repelled. The interacting force is a nonlinear function of the hammer compression. The final piano model is a coupled system of partial differential equations, each of them exhibiting specific difficulties (nonlinear nature of the string system of equations, frequency dependent damping of the soundboard, great number of unknowns required for the acoustic propagation), in addition to couplings' inherent difficulties.Mathematics Subject Classification. 00A71, 00A65, 65P05, 65N25, 35Q72, 35L05.

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Section: Introductionmentioning

confidence: 99%

Time domain simulation of a piano. Part 1: model description

2014

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“…The present studies focus the analysis and evaluation of the High Pitch Effect, Low Pitch Effect, FIIR Effect, and IIR Effect based on spectrum of the voice signal. It is noticed that comparison based on the parameters, spectrum can clearly shows the difference between original and effected signal as illustrated in Figures [12]- [15]. We thus conclude that the variation in the VSA parameters yields a relative comparison of constitutional and unconstitutional voice of N-bit signals can be done.…”

Section: Results Analysis For Iir Effect Analysismentioning

confidence: 62%

“…In doing so, we have introduce IIR effect. The comparative Spectrum of with and without IIR effect is shown in fig.- [15]. The difference between without IIR effect and with IIR effect is further illustrated in Fig.…”

Section: Results Analysis For Iir Effect Analysismentioning

confidence: 92%

Analysis of Voice Signal Based On High Pitch, Low Pitch, FIIR and IIR Spectrum Suitable For Pervasive Computing Using MatLab

Chaudhari¹,

Kumar²,

Singh³

et al. 2010

IJCEE

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“…In waveguide models, inharmonicity is typically modeled with allpass filters. [36][37][38][39] In this study the inharmonicity is synthesized with a chain of four second-order allpass filters for low tones ͑349Ͻ Hz͒ and with a single second-order allpass filter for high tones ͑349 Ͼ Hz͒.…”

Section: Inharmonicitymentioning

confidence: 99%

Model-based sound synthesis of the guqin

Penttinen¹,

Pakarinen²,

Välimäki³

et al. 2006

The Journal of the Acoustical Society of America

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This paper presents a model-based sound synthesis algorithm for the Chinese plucked string instrument called the guqin. The instrument is fretless, which enables smooth pitch glides from one note to another. A version of the digital waveguide synthesis approach is used, where the string length is time-varying and its energy is scaled properly. A body model filter is placed in cascade with the string model. Flageolet tones are synthesized with the so-called ripple filter structure, which is an FIR comb filter in the delay line of a digital waveguide model. In addition, signal analysis of recorded guqin tones is presented. Friction noise produced by gliding the finger across the soundboard has a harmonic structure and is proportional to the gliding speed. For pressed tones, one end of a vibrating string is terminated either by the nail of the thumb or a fingertip. The tones terminated with a fingertip decay faster than those terminated with a thumb. Guqin tones are slightly inharmonic and they exhibit phantom partials. The synthesis model takes into account these characteristic features of the instrument and is able to reproduce them. The synthesis model will be used for rule based synthesis of guqin music.

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Physically Informed Signal Processing Methods for Piano Sound Synthesis: A Research Overview

Cited by 25 publications

References 28 publications

Time domain simulation of a piano. Part 1: model description

Time domain simulation of a piano. Part 1: model description

Analysis of Voice Signal Based On High Pitch, Low Pitch, FIIR and IIR Spectrum Suitable For Pervasive Computing Using MatLab

Model-based sound synthesis of the guqin

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