Investigating Clarinet Articulation Using a Physical Model and an Artificial Blowing Machine

A time-domain physical model is presented that is capable of simulating a variety of articulation techniques in single-reed woodwind instruments and suitable for real-time sound synthesis. Due to the nonlinear nature of the excitation mechanism, an energy-based approach is adopted for the construction of the numerical scheme in order to ensure algorithm stability. To validate the model, measurements are carried out using an artificial blowing machine. The construction of the machine, including a sensor-equipped reed and mouthpiece as well as an automated artificial tongue and lip, is described in detail. By adjusting the motion of the tongue, the blowing machine can generate audio signals corresponding to portato and staccato articulation. These signals are resynthesised following an inverse modelling approach based on the presented physical model, during which model parameters are estimated. All estimated parameters lie in a physically feasible range and may be used for sound synthesis and sound analysis applications.

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Estimation of hydrological response of a small Mediterranean watershed to fire by data analysis and a modelling approach

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-364-64-2014 ◽

2014 ◽

Vol 364 ◽

pp. 64-69 ◽

Cited By ~ 1

Author(s):

L. Lebedeva ◽

O. Semenova ◽

N. Folton

Keyword(s):

Data Analysis ◽

Hydrological Regime ◽

Model Parameters ◽

Hydrological Response ◽

Time Step ◽

Estimated Parameters ◽

Nonlinear Character ◽

Abstract Data ◽

Modelling Approach ◽

Fire Conditions

Abstract. Data analysis and amodelling approach were used to detect the changes in hydrological regime in the Rimbaud watershed (France) after the fire in 1990. It was revealed that the increase of peak discharges was only observed during three years after the fire in the wet period of the year, at an hourly time scale. The Hydrograph model was applied for continuous runoff simulations at an hourly time step for the period 1967–2004. The parameters assessed for pre-fire conditions and used without change for the post-fire period satisfactorily fit the whole period of simulations with mean Nash-Sutcliffe efficiency 0.52. The set of model parameters representing the post-fire conditions of changing environment was developed. Based on newly estimated parameters, the efficiency of simulations of selected outstanding flood peaks was improved. However, overall model representation for the post-fire period (1990–1992) has declined. It is concluded that discernible fire impact is only localized on separate floods events and that it has a nonlinear character.

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Statistical Analysis of Manufacturer Difference for Miniature Triode Tube Characteristics Based on Physical Model Parameters

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.140.1 ◽

2020 ◽

Vol 140 (1) ◽

pp. 1-8

Author(s):

Masaki Inui ◽

Reo Sasaki ◽

Toshihiko Hamasaki

Keyword(s):

Statistical Analysis ◽

Physical Model ◽

Model Parameters

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Sparse pursuit and dictionary learning for blind source separation in polyphonic music recordings

EURASIP Journal on Audio Speech and Music Processing ◽

10.1186/s13636-020-00190-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Sören Schulze ◽

Emily J. King

Keyword(s):

Single Channel ◽

Spectral Characteristics ◽

Musical Instruments ◽

Model Parameters ◽

Blind Separation ◽

Audio Signals ◽

Frequency Spectra ◽

Acoustic Instruments ◽

Invariant Properties ◽

Music Recordings

AbstractWe propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of pitch. We develop a novel sparse pursuit algorithm that can match the discrete frequency spectra from the recorded signal with the continuous spectra delivered by the model. We first use this algorithm to convert an STFT spectrogram from the recording into a novel form of log-frequency spectrogram whose resolution exceeds that of the mel spectrogram. We then make use of the pitch-invariant properties of that representation in order to identify the sounds of the instruments via the same sparse pursuit method. As the model parameters which characterize the musical instruments are not known beforehand, we train a dictionary that contains them, using a modified version of Adam. Applying the algorithm on various audio samples, we find that it is capable of producing high-quality separation results when the model assumptions are satisfied and the instruments are clearly distinguishable, but combinations of instruments with similar spectral characteristics pose a conceptual difficulty. While a key feature of the model is that it explicitly models inharmonicity, its presence can also still impede performance of the sparse pursuit algorithm. In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset. Additionally, we demonstrate that the dictionary that is constructed for one recording can be applied to a different recording with similar instruments without additional training.

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Sequential modelling of the Earth’s core magnetic field

Earth Planets and Space ◽

10.1186/s40623-020-01230-1 ◽

2020 ◽

Vol 72 (1) ◽

Cited By ~ 3

Author(s):

Guillaume Ropp ◽

Vincent Lesur ◽

Julien Baerenzung ◽

Matthias Holschneider

Keyword(s):

Magnetic Field ◽

Prior Information ◽

Spatial Scales ◽

Model Parameters ◽

International Geomagnetic Reference Field ◽

The Earth ◽

Temporal And Spatial ◽

Different Sources ◽

Original Approach ◽

Modelling Approach

Abstract We describe a new, original approach to the modelling of the Earth’s magnetic field. The overall objective of this study is to reliably render fast variations of the core field and its secular variation. This method combines a sequential modelling approach, a Kalman filter, and a correlation-based modelling step. Sources that most significantly contribute to the field measured at the surface of the Earth are modelled. Their separation is based on strong prior information on their spatial and temporal behaviours. We obtain a time series of model distributions which display behaviours similar to those of recent models based on more classic approaches, particularly at large temporal and spatial scales. Interesting new features and periodicities are visible in our models at smaller time and spatial scales. An important aspect of our method is to yield reliable error bars for all model parameters. These errors, however, are only as reliable as the description of the different sources and the prior information used are realistic. Finally, we used a slightly different version of our method to produce candidate models for the thirteenth edition of the International Geomagnetic Reference Field.

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Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic

10.1101/2020.05.30.20117754 ◽

2020 ◽

Cited By ~ 1

Author(s):

Leila Taghizadeh ◽

Ahmad Karimi ◽

Clemens Heitzinger

Keyword(s):

Inverse Modeling ◽

Posteriori Probability ◽

Model Parameters ◽

Epidemiological Models ◽

Unknown Parameters ◽

Protective Measures ◽

A Posteriori Probability ◽

Estimated Parameters ◽

Forward And Inverse Modeling ◽

Inversion Techniques

AbstractThe main goal of this paper is to develop the forward and inverse modeling of the Coronavirus (COVID-19) pandemic using novel computational methodologies in order to accurately estimate and predict the pandemic. This leads to governmental decisions support in implementing effective protective measures and prevention of new outbreaks. To this end, we use the logistic equation and the SIR system of ordinary differential equations to model the spread of the COVID-19 pandemic. For the inverse modeling, we propose Bayesian inversion techniques, which are robust and reliable approaches, in order to estimate the unknown parameters of the epidemiological models. We use an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. Furthermore, we present a fatality analysis for COVID-19 in Austria, which is also of importance for governmental protective decision making. We perform our analyses on the publicly available data for Austria to estimate the main epidemiological model parameters and to study the effectiveness of the protective measures by the Austrian government. The estimated parameters and the analysis of fatalities provide useful information for decision makers and makes it possible to perform more realistic forecasts of future outbreaks.

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A Population Modelling Approach to Studying Age-Related Effects on Excitation-Contraction Coupling in Human Cardiomyocytes

10.1101/2019.12.17.877514 ◽

2019 ◽

Author(s):

Arsenii Dokuchaev ◽

Svyatoslav Khamzin ◽

Olga Solovyova

Keyword(s):

Experimental Data ◽

Transient Outward Current ◽

Model Parameters ◽

Maximal Velocity ◽

Population Modelling ◽

Individual Parameter ◽

Human Cardiomyocytes ◽

Age Related ◽

Age Related Changes ◽

Modelling Approach

AbstractAgeing is the dominant risk factor for cardiovascular diseases. A great body of experimental data has been gathered on cellular remodelling in the Ageing myocardium from animals. Very few experimental data are available on age-related changes in the human cardiomyocyte. We have used our combined electromechanical model of the human cardiomyocyte and the population modelling approach to investigate the variability in the response of cardiomyocytes to age-related changes in the model parameters. To generate the model population, we varied nine model parameters and excluded model samples with biomarkers falling outside of the physiological ranges. We evaluated the response to age-related changes in four electrophysiological model parameters reported in the literature: reduction in the density of the K+ transient outward current, maximal velocity of SERCA, and an increase in the density of NaCa exchange current and CaL-type current. The sensitivity of the action potential biomarkers to individual parameter variations was assessed. Each parameter modulation caused an increase in APD, while the sensitivity of the model to changes in GCaL and Vmax_up was much higher than to those in the effects of Gto and KNaCa. Then 60 age-related sets of the four parameters were randomly generated and each set was applied to every model in the control population. We calculated the frequency of model samples with repolarisation anomalies (RA) and the shortening of the electro-mechanical window in the ageing model populations as an arrhythmogenic ageing score. The linear dependence of the score on the deviation of the parameters showed a high determination coefficient with the most significant impact due to the age-related change in the CaL current. The population-based approach allowed us to classify models with low and high risk of age-related RA and to predict risks based on the control biomarkers.

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Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v9.i3.pp696-702 ◽

2018 ◽

Vol 9 (3) ◽

pp. 696 ◽

Cited By ~ 2

Author(s):

Byamakesh Nayak ◽

Sangeeta Sahu ◽

Tanmoy Roy Choudhury

Keyword(s):

Experimental Data ◽

Transfer Function ◽

Adaptive Method ◽

Dc Motor ◽

Model Parameters ◽

Current Response ◽

Adaptive Model ◽

Unknown Parameters ◽

Dc Motors ◽

Estimated Parameters

<p>This paper explains an adaptive method for estimation of unknown parameters of transfer function model of any system for finding the parameters. The transfer function of the model with unknown model parameters is considered as the adaptive model whose values are adapted with the experimental data. The minimization of error between the experimental data and the output of the adaptive model have been realised by choosing objective function based on different error criterions. Nelder-Mead optimisation Method is used for adaption algorithm. To prove the method robustness and for students learning, the simple system of separately excited dc motor is considered in this paper. The experimental data of speed response and corresponding current response are taken and transfer function parameters of dc motors are adapted based on Nelder-Mead optimisation to match with the experimental data. The effectiveness of estimated parameters with different objective functions are compared and validated with machine specification parameters.</p>

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Selection of catchment descriptors for the physical similarity approach. Part II: Application

Soil and Water Research ◽

10.17221/23/2012-swr ◽

2013 ◽

Vol 8 (No. 4) ◽

pp. 186-194

Author(s):

M. Heřmanovský ◽

P. Pech

Keyword(s):

Land Cover ◽

A Priori ◽

Model Parameters ◽

Physical Similarity ◽

Model Parameter ◽

Soil Moisture Accounting ◽

Estimated Parameters ◽

Selection Of

This paper demonstrates an application of the previously published method for selection of optimal catchment descriptors, according to which similar catchments can be identified for the purpose of estimation of the Sacramento – Soil Moisture Accounting (SAC-SMA) model parameters for a set of tested catchments, based on the physical similarity approach. For the purpose of the analysis, the following data from the Model Parameter Estimation Experiment (MOPEX) project were taken: a priori model parameter sets used as reference values for comparison with the newly estimated parameters, and catchment descriptors of four categories (climatic descriptors, soil properties, land cover and catchment morphology). The inverse clustering method, with Andrews’ curves for a homogeneity check, was used for the catchment grouping process. The optimal catchment descriptors were selected on the basis of two criteria, one comparing different subsets of catchment descriptors of the same size (MIN), the other one evaluating the improvement after addition of another catchment descriptor (MAX). The results suggest that the proposed method and the two criteria used may lead to the selection of a subset of conditionally optimal catchment descriptors from a broader set of them. As expected, the quality of the resulting subset of optimal catchment descriptors is mainly dependent on the number and type of the descriptors in the broader set. In the presented case study, six to seven catchment descriptors (two climatic, two soil and at least two land-cover descriptors) were identified as optimal for regionalisation of the SAC-SMA model parameters for a set of MOPEX catchments.

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Wear-Factor Prediction Based on Data-Driven Inversion Technique for Casing Wear Estimation

Volume 11: Petroleum Technology ◽

10.1115/omae2020-19035 ◽

2020 ◽

Author(s):

Manish K. Mittal ◽

Robello Samuel ◽

Aldofo Gonzales

Keyword(s):

Physical Model ◽

Factor Model ◽

Data Driven ◽

Wear Volume ◽

Model Parameters ◽

Wear Factor ◽

Side Force ◽

Inversion Technique ◽

Casing Wear ◽

Volume Output

Abstract Wear factor is an important parameter for estimating casing wear, yet the industry lacks a sufficient data-driven wear-factor prediction model based on previous data. Inversion technique is a data-driven method for evaluating model parameters for a setting wherein the input and output values for the physical model/equation are known. For this case, the physical equation to calculate wear volume has wear factor, side force, RPM, tool-joint diameter, and time for a particular operation (i.e., rotating on bottom, rotating off bottom, sliding, back reaming, etc.) as inputs. Except for wear factor, these values are either available or can be calculated using another physical model (wear-volume output is available from the drilling log). Wear factor is considered the model parameter and is estimated using the inversion technique method. The preceding analysis was performed using soft-string and stiff-string models for side-force calculations and by considering linear and nonlinear wear-factor models. An iterative approach was necessary for the nonlinear wear-factor model because of its complexity. Log data provide the remaining thickness of the casing, which was converted into wear volume using standard geometric calculations. A paper [1] was presented in OMC 2019 discussing a method for bridging the gap. A study was conducted in this paper for a real well based on the new method, and successful results were discussed. The current paper extends that study to another real well casing wear prediction with this novel approach. Some methods discussed are already included in the mentioned paper.

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