Computer modeling and auralization in room acoustics: An overview on computer models for room acoustics

1992 ◽  
Vol 92 (4) ◽  
pp. 2345-2345
Author(s):  
Michael Klasco
2020 ◽  
Vol 16 (3) ◽  
pp. 194-200
Author(s):  
Shai Luria

Computer modeling of the wrist has followed other fields in the search for descriptive methods to understand the biomechanics of injury. Using patient-specific 3D computer models, we may better understand the biomechanics of wrist fractures in order to plan better care. We may better estimate fracture morphology and stability and evaluate surgical indications, design more adequate or effective surgical approaches and develop novel methods of therapy. The purpose of this review is to question the actual advances made in the understanding of wrist fractures using computer models.


2018 ◽  
Vol 5 (2) ◽  
pp. 247-256
Author(s):  
Paul Thagard

Why do people have conflicting views of equality concerning the distribution of income, wealth, and satisfaction of vital needs? How do people form and sometimes change their views of equality and related issues, such as gender identity? Answers to such questions can benefit from cognitive science—the interdisciplinary field that includes neuroscience and computer modeling as well as psychology. According to principles of emotional coherence, attitudes develop and change because of connections among the values attached to systems of concepts, beliefs, and goals. People attach a positive value to concepts such as equality, if the concept fits with other positive concepts such as human needs, and opposes negative concepts such as poverty. Emotional coherence balances positive and negative values to yield an overall conclusion. Computer models based on emotional coherence explain people’s differing attitudes about equality and issues such as transgender rights. They also model how people sometimes change their minds.


Author(s):  
Heather L. Lai ◽  
Brian Hamilton

Abstract This paper investigates the use of two room acoustics metrics designed to evaluate the degree to which the linearity assumptions of the energy density curves are valid. The study focuses on measured and computer-modeled energy density curves derived from the room impulse response of a space exhibiting a highly non-diffuse sound field due to flutter echo. In conjunction with acoustical remediation, room impulse response measurements were taken before and after the installation of the acoustical panels. A very dramatic decrease in the reverberation time was experienced due to the addition of the acoustical panels. The two non-linearity metrics used in this study are the non-linearity parameter and the curvature. These metrics are calculated from the energy decay curves computed per octave band, based on the definitions presented in ISO 3382-2. The non-linearity parameter quantifies the deviation of the EDC from a straight line fit used to generated T20 and T30 reverberation times. Where the reverberation times are calculated based on a linear regression of the data relating to either −5 to −25 dB for T20 or −5 to −35 dB for T30 reverberation time calculations. This deviation is quantified using the correlation coefficient between the energy decay curve and the linear regression for the specified data. In order to graphically demonstrate these non-linearity metrics, the energy decay curves are plotted along with the linear regression curves for the T20 and T30 reverberation time for both the measured data and two different room acoustics computer-modeling techniques, geometric acoustics modeling and finite-difference wave-based modeling. The intent of plotting these curves together is to demonstrate the relationship between these metrics and the energy decay curve, and to evaluate their use for quantifying degree of non-linearity in non-diffuse sound fields. Observations of these graphical representations are used to evaluate the accuracy of reverberation time estimations in non-diffuse environments, and to evaluate the use of these non-linearity parameters for comparison of different computer-modeling techniques or room configurations. Using these techniques, the non-linearity parameter based on both T20 and T30 linear regression curves and the curvature parameter were calculated over 250–4000 Hz octave bands for the measured and computer-modeled room impulse response curves at two different locations and two different room configurations. Observations of these calculated results are used to evaluate the consistency of these metrics, and the application of these metrics to quantifying the degree of non-linearity of the energy decay curve derived from a non-diffuse sound field. These calculated values are also used to evaluate the differences in the degree of diffusivity between the measured and computer-modeled room impulse response. Acoustical computer modeling is often based on geometrical acoustics using ray-tracing and image-source algorithms, however, in non-diffuse sound fields, wave based methods are often able to better model the characteristic sound wave patterns that are developed. It is of interest to study whether these improvements in the wave based computer-modeling are also reflected in the non-linearity parameter calculations. The results showed that these metrics provide an effective criteria for identifying non-linearity in the energy decay curve, however for highly non-diffuse sound fields, the resulting values were found to be very sensitive to fluctuations in the energy decay curves and therefore, contain inconsistencies due to these differences.


2006 ◽  
Vol 120 (5) ◽  
pp. 2998-2998 ◽  
Author(s):  
U. Peter Svensson ◽  
Paul T. Calamia

1997 ◽  
Vol 4 (4) ◽  
pp. 229-246 ◽  
Author(s):  
Michael Vorländer

In the last decade computer simulations of sound fields in rooms have been developed for application in research and consulting. Some programs are commercially available. Most computer models are based on geometrical room acoustics and/or on statistical (radiosity) methods, thus not including wave phenomena such as diffraction. The uncertainty of typical simulation software was investigated in an international verification test in 1994 and 1995. The results were partly promising although some programs were not as reliable as the operators expected. These round robin tests have been continued until today with simulations and measurements in a concert hall in Jönköping in Sweden. In this paper the basic algorithms of room acoustical computer simulations, the verification in round robin tests and the observed accuracy and limitations are summarised. Finally, possible improvements are discussed.


Author(s):  
Judi E. See ◽  
Michael A. Vidulich

The predictive validity of computer simulation modeling of operator mental workload and situational awareness (SA) during a simulated air-to-ground combat mission was assessed in the present study. In Phase I, 12 participants completed a series of combat missions in a laboratory flight simulator and provided subjective ratings of workload (using the SWAT) and SA (using the SART). In Phase II, computer models of the mission were constructed using the Micro Saint modeling tool. The visual, auditory, kinesthetic, cognitive, and psychomotor components of the workload associated with each task were estimated and used to obtain measures of average and peak workload. The results from the simulated combat missions versus the Micro Saint models were similar but not identical, indicating that the computer models were partially but not completely valid predictors of mental workload and SA. The computer modeling appeared to be a more effective predictor of SA rather than mental workload.


2007 ◽  
Vol 121 (5) ◽  
pp. 3152-3152
Author(s):  
Gary W. Siebein ◽  
Robert M. Lilkendey ◽  
Hyun Paek ◽  
Edwin S. Skorski

2005 ◽  
Vol 12 (3) ◽  
pp. 175-188 ◽  
Author(s):  
Sabeer H. Mir ◽  
Adel A. Abdou

Advancements in information and instruction technology have led to the evolution of a new type of classroom referred to as “smart classrooms”. These have enhanced audio-visual equipment, computers and seating layouts designed to facilitate interactive learning. Placement of different sound-absorbing finishes in an efficient manner improves the listening conditions within the classroom and reduces the amplification of internally generated noise such as that from computers and instructional equipment. This study investigates the best overall configuration of sound-absorbing material placement and characteristics of surface treatment in an attempt to enhance the listening conditions in smart classrooms. A typical layout of a smart classroom was modeled and simulated using room acoustics computer modeling. Acoustics indicators such as Reverberation Time (RT), Sound Clarity (C50) and Speech Transmission Index (STI) were used for comparing alternative cases in optimising sound-absorbing material characteristics and placement. Additionally, measurements were conducted in similar classrooms to assess the magnitude and characteristics of generated noise. To determine the impact of the resulting background noise simulations were carried out. The resulting configuration of sound-absorbing material for a typical smart classroom can also be utilized by architects and educational institutions to enhance the acoustics of existing conventional classrooms in the process of being converted or upgraded.


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