Generating and Modifying Melody Using Editable Noise Function

2006 ◽  
pp. 164-168 ◽  
Author(s):  
Yong-Woo Jeon ◽  
In-Kwon Lee ◽  
Jong-Chul Yoon
Keyword(s):  
Noise Function

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

Mean-Square Response of a Second-Order System to Nonstationary, Random Excitation

1970 ◽  
Vol 37 (3) ◽  
pp. 612-616 ◽  
Author(s):  
L. L. Bucciarelli ◽  
C. Kuo
Keyword(s):  
Narrow Band ◽  
Random Excitation ◽  
Second Order ◽  
Envelope Function ◽  
Order System ◽  
Mean Square ◽  
Forcing Function ◽  
Mean Square Response ◽  
Noise Function ◽  
The Mean

The mean-square response of a lightly damped, second-order system to a type of non-stationary random excitation is determined. The forcing function on the system is taken in the form of a product of a well-defined, slowly varying envelope function and a noise function. The latter is assumed to be white or correlated as a narrow band process. Taking advantage of the slow variation of the envelope function and the small damping of the system, relatively simple integrals are obtained which approximate the mean-square response. Upper bounds on the mean-square response are also obtained.

Relationship between a Wiener–Hermite expansion and an energy cascade

1970 ◽  
Vol 41 (2) ◽  
pp. 387-403 ◽  
Author(s):  
S. C. Crow ◽  
G. H. Canavan
Keyword(s):  
Velocity Field ◽  
White Noise ◽  
Burgers Equation ◽  
Higher Order ◽  
Energy Cascade ◽  
Wave Number ◽  
Initial State ◽  
Hermite Expansion ◽  
Higher Order Terms ◽  
Noise Function

Meecham and his co-workers have developed a theory of turbulence involving a truncated Wiener–Hermite expansion of the velocity field. The randomness is taken up by a white-noise function associated, in the original version of the theory, with the initial state of the flow. The mechanical problem then reduces to a set of coupled integro-differential equations for deterministic kernels. We have solved numerically an analogous set for Burgers's model equation and have computed, for the sake of comparison, actual random solutions of the Burgers equation. We find that the theory based on the first two terms of the Wiener–Hermite expansion predicts an insufficient rate of energy decay for Reynolds numbers larger than two, because the equations for the kernels contain no convolution integrals in wave-number space and therefore permit no cascade of energy. An energy cascade in wave-number space corresponds to a cascade up through successive terms of the Wiener-Hermite expansion. Pictures of the Gaussian and non-Gaussian components of an actual solution of the Burgers equation show directly that only higher-order terms in the Wiener–Hermite expansion are capable of representing shocks, which dissipate the energy. Higher-order terms would be needed even for a nearly Gaussian field of evolving three-dimensional turbulence. ‘Gaussianity’, in the experimentalist's sense, has no bearing on the rate of convergence of a Wiener–Hermite expansion whose white-noise function is associated with the initial state. Such an expansion would converge only if the velocity field and its initial state were joint-normally distributed. The question whether a time-varying white-noise function can speed the convergence is treated in the paper following this one.

Modelling Material Microstructure Using the Perlin Noise Function

2020 ◽  
Author(s):  
F. Conde‐Rodríguez ◽  
Á‐.L. García‐Fernández ◽  
J.C. Torres
Keyword(s):  
Material Microstructure ◽  
Perlin Noise ◽  
Noise Function

Parametric Optimization for the Design of Passenger Vehicle Suspension System with the Application of Genetic Algorithm

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Mohd Avesh ◽  
Rajeev Srivastava ◽  
Rakesh Chandmal Sharma
Keyword(s):  
Suspension System ◽  
Vehicle Suspension ◽  
Passenger Vehicle ◽  
Multiple Parameters ◽  
Car Model ◽  
Optimum Parameters ◽  
Floor Vibrations ◽  
Vehicle Acceleration ◽  
Noise Function ◽  
Vehicle Suspension System

In this paper an improved suspension system of a four-wheel vehicle is designed to minimize the vehicle floor vibrations. A seven degree-of-freedom full car model of vehicle system is modelled using the linear approach and is excited under the uncertain road inputs approximated by the white noise function. Vehicle acceleration in bounce, pitch and roll along with suspension displacement are the multiple parameters blended into single objective function to be minimized through proper allocation of weightages to each sub-objective based on real implications. The modified suspension system with optimum parameters results in improvement in the dynamic characteristic. Computer simulation through MATLAB-Simulink is providing an approximate solution against expensive and time taking experimentation.

Identification of Sound Quality Parameters With Respect to Subjective Feel of HVAC Noise of Diesel SUV’s

2012 ◽  
Author(s):  
Murali Bodla ◽  
Riyazuddin Mohammed ◽  
Rajesh Bhangale ◽  
Khumbhar Mansinh
Keyword(s):  
Power Plants ◽  
Sound Quality ◽  
Quality Parameters ◽  
Climatic Conditions ◽  
Operating Conditions ◽  
Subjective Rating ◽  
Sound Sources ◽  
Cabin Noise ◽  
Noise Function ◽  
Noise Measurements

Recent trends in developing quieter diesel power plants in automobiles leading to unmasking the secondary sound sources. One of the major secondary sound sources of in-cabin noise is HVAC system. HVAC noise is one such sound likely to be present as long as the automobile is in use. In extreme climatic conditions, like in India, HVAC is majorly operated at higher speeds and adding to that SUV volume requires more air circulation which generates lot of flow induced noise. Under these conditions, the contribution of HVAC noise is more significant for passengers and many a times it influences subjective cognition that causes the driver’s emotional response to be unpleasant, it is more important to identify the most significant sound quality parameters that contribute to the perception of HVAC noise. Measurements were done on five different diesel SUVs with different HVAC operating conditions each having variable fan speeds with engine on and off respectively. Using the semantic differential technique subjective rating of the measured signals has been done. The psycho acoustic parameters calculated objectively for the measured interior sounds later those were compared with subjective rating by using HVAC noise function (HNF) to obtain the suitable parameters to represent HVAC noise.

scholarly journals Prime gradient noise

2021 ◽  
Author(s):  
Sheldon Taylor ◽  
Owen Sharpe ◽  
Jiju Peethambaran
Keyword(s):  
Prime Numbers ◽  
Polar Coordinate System ◽  
Pattern Synthesis ◽  
Procedural Modeling ◽  
Perlin Noise ◽  
Noise Function ◽  
Polar Coordinate ◽  
Prime Sequence ◽  
Fractal Noise ◽  
Unit Vectors

AbstractProcedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns. Ideally, a procedural noise function should be compact, aperiodic, parameterized, and randomly accessible. Traditional lattice noise functions such as Perlin noise, however, exhibit periodicity due to the axial correlation induced while hashing the lattice vertices to the gradients. In this paper, we introduce a parameterized lattice noise called prime gradient noise (PGN) that minimizes discernible periodicity in the noise while enhancing the algorithmic efficiency. PGN utilizes prime gradients, a set of random unit vectors constructed from subsets of prime numbers plotted in polar coordinate system. To map axial indices of lattice vertices to prime gradients, PGN employs Szudzik pairing, a bijection F: ℕ2 → ℕ. Compositions of Szudzik pairing functions are used in higher dimensions. At the core of PGN is the ability to parameterize noise generation though prime sequence offsetting which facilitates the creation of fractal noise with varying levels of heterogeneity ranging from homogeneous to hybrid multifractals. A comparative spectral analysis of the proposed noise with other noises including lattice noises show that PGN significantly reduces axial correlation and hence, periodicity in the noise texture. We demonstrate the utility of the proposed noise function with several examples in procedural modeling, parameterized pattern synthesis, and solid texturing.

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