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Equal Loudness Level Curves.
Source publication
Resonances in small rooms can yield inadequate frequency response. In listening rooms, they may cause unwanted coloration. It is possible to choose the correct shape and room dimensions in order to reduce their effects. However, the room warmth may be badly affected if the low-frequency sound field is inadequately diminished. In this paper an objec...
Context in source publication
Context 1
... problem will be addressed from the field of psychoacoustic instead of the architectural and physical acoustic ones. In this aspect the method to be presented is characterized by searching for the room dimensions which produce equal loudness at the low frequency bands, i.e. the sound pressure level should agree -as close as possible-with some of the loudness curves shown in Fig.1. These curves represent the response of the human auditory system as function of sound pressure and frequency. ...
Citations
... Although the standard deviation is a good metric for recording studios, where coloration from the room is not desired, there are other options that might better suit recreational listening environments, such as the one used by Floody and Venegas [12], which uses the Equal Loudness Curves to achieve an isophonically flat response. ...
In small room acoustics, the range from the first resonant frequency up to the Schroeder frequency is dominated by modal resonances and the Speaker-Boundary Interference Response (SBIR). Both are very sensitive to the positioning of sources, receivers and room geometry. The source locations determine which modes are excited , and the listener locations determine which modes are heard. In past years, various iterative optimization programs emerged to separately determine the optimal room ratios, sources and listening positions of perfectly reflective cuboid rooms, through the use of the image-source model. Despite its fast computation times, this approach does not account for scattering, phase change at the boundary and cannot be extended to non-cuboid rooms. The present work presents a solution to those issues by using the Boundary Element Method (BEM) to compute the frequency response at low-frequencies, considering the effects of the boundary's complex admittance and all acoustical elements inside the room. With BEM as its engine, a Room Optimization Genetic Algorithm (ROGA) was developed to optimize source and receiver positions simultaneously with the room geometry, aiming to present the best possible acoustic environment given imposed restraints. To control the room's temporal decay, low-frequency acoustic treatments were added to the BEM model. By using Transfer Matrix Models, the acoustical behavior of different multilayered treatments can be modeled and inserted into the BEM simulation to evaluate the change in the room's acoustic field and in the frequency response at the receiving positions. 3D waterfall plots illustrate the temporal decay following optimization. Examples will be presented.
... The proposed method takes advantage of approaches presented by [5], [9] and [13] to create an objective function that aims to characterize the limitations of human hearing, avoiding large deviations in the spectrum and assessing common listening positions of small spaces. Hence, this initial investigation is carried with the neural network proposed by [9] to determine the loudness curve associated to the frequency response of several source-receiver configurations, and proceedes to assess their quality using the Figure of Merit (FOM), described in [13]. ...
... The proposed method takes advantage of approaches presented by [5], [9] and [13] to create an objective function that aims to characterize the limitations of human hearing, avoiding large deviations in the spectrum and assessing common listening positions of small spaces. Hence, this initial investigation is carried with the neural network proposed by [9] to determine the loudness curve associated to the frequency response of several source-receiver configurations, and proceedes to assess their quality using the Figure of Merit (FOM), described in [13]. A genetic algorithm is used to minimize the objective function given a set of bounds that must be respected and a appropriate search step, that ensures that only significant changes are made to the geometry. ...
... A feed-forward neural network (NN) was developed to represent loudness as a function of frequency and sound pressure level (SPL). Later, the same authors described a shape optimization methodology on complex geometries [9], using a evolution of the NN proposed previously and computing the pressure with the finite elements method. The work is based on minimizing spacial variance over homogeneously distributed points, with parallel floor and ceiling. ...
Small rooms are inherently problematic in the low frequency range. EBU 3267 recommends a minimum of 30m2 for high-quality sound control rooms, which is not always available. The present study aims to aid the design of non-rectangular small critical listening spaces with sub-optimal floor areas by applying genetic algorithms to a modal Finite Elements Method simulation framework. The resonant frequencies and eigenfunctions are computed for the enclosure by the numerical method and modal decomposition model is used to predict frequency response for intended source-listener positions. A psychoacoustic model is used to account for human hearing limitations. Optimization is carried with the Figure of Merit metric for weighted receiver positions. The proposed geometry is compared to standard recommendations and appraised under the scope of common objective functions used to evaluate critical listening quality such as the Bonello criteria and standard deviation. The optimal room proposed mitigates large deviations in the spectrum, fulfilling the objective.
... In these works, the fluctuations of the sound pressure level have been minimized. Instead, Floody and Venegas [9][10] have proposed the optimization of the room dimensions based on minimizing the loudness level fluctuations. In this work, these two approaches are used to optimize the dimensions of slit resonators. ...
... The second objective function has the goal of obtaining the best psychoacoustic response of the room. This new function to minimize corresponds to the standard deviation of the loudness level in the frequency range previously mentioned [9,10]. This objective function has been successfully used in the design of the room's geometry [9,10]. ...
... This new function to minimize corresponds to the standard deviation of the loudness level in the frequency range previously mentioned [9,10]. This objective function has been successfully used in the design of the room's geometry [9,10]. ...
The present article presents a method to redistribute the acoustic modes of a rectangular enclosure in the low frequency range using slit resonators. The objective of the present work is to compare different strategies of optimal design in order to determine the dimensions of the resonators. The method of the finite elements will be used to model the acoustic physical behavior of the room. In addition a neuronal network will estimate the loudness level perceived by the auditor. The different strategies of design are: First, a strategy of design will be implemented based on the minimization of the fluctuations of the sound level pressure. Second, the optimization will be based on the diminution of the variations of the loudness level. Finally, two methods of optimization, genetic algorithm and differential evolution will be compared. The three different strategies from optimization will be compared generally and of it will determine the design variables that are critics in this process.
