A Requirements-Driven Optimization Method for Acoustic Liners Using Analytic Derivatives

More than ever, there is flexibility and freedom in acoustic liner design. Subject to practical considerations, liner design variables may be manipulated to achieve a target attenuation spectrum. But characteristics of the ideal attenuation spectrum can be difficult to know. Many multidisciplinary system effects govern how engine noise sources contribute to community noise. Given a hardwall fan noise source to be suppressed, and using an analytical certification noise model to compute a community noise measure of merit, the optimal attenuation spectrum can be derived using multidisciplinary systems analysis methods. In a previous paper on this subject, a method deriving the ideal target attenuation spectrum that minimizes noise perceived by observers on the ground was described. A simple code-wrapping approach was used to evaluate a community noise objective function for an external optimizer. Gradients were evaluated using a finite difference formula. The subject of this paper is an application of analytic derivatives that supply precise gradients to an optimization process. Analytic derivatives improve the efficiency and accuracy of gradient-based optimization methods and allow consideration of more design variables. In addition, the benefit of variable impedance liners is explored using a multi-objective optimization

Stated choice valuation of traffic related noise

This paper reports a novel application of the stated choice method to the valuation of road traffic noise. The innovative context used is that of choice between apartments with different levels of traffic noise, view, sunlight and cost with which respondents would be familiar. Stated choice models were developed on both perceived and objective measures of traffic noise, with the former statistically superior, and an extensive econometric analysis has been conducted to assess the nature and extent of householders’ heterogeneity of preferences for noise. This found that random taste variation is appreciable but also identified considerable systematic variation in valuations according to income level, household composition and exposure to noise. Self-selectivity is apparent, whereby those with higher marginal values of noise tend to live in quieter apartments. Sign and reference effects were apparent in the relationship between ratings and objective noise measures, presumably reflecting the non-linear nature of the latter. However, there was no strong support for sign, size or reference effects in the valuations of perceived noise levels

Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply non- linear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The scene- and-process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-process- dependent Modulation Transfer Functions (SPD-MTF)

The Mystique of Central Bank Speak

Despite the recent trend toward greater transparency of monetary policy, in many respects mystique still prevails in central bank speak. This paper shows that the resulting perception of ambiguity could be desirable. Under the plausible assumption of imperfect common knowledge about the degree of central bank transparency, economic outcomes are affected by both the actual and perceived degree of transparency. It is shown that actual transparency is beneficial, while it may be useful to create the perception of opacity. The optimal communication strategy for the central bank is to provide clarity about the inflation target and to communicate information about the output target and supply shocks with perceived ambiguity. In this respect, the central bank benefits from sustaining transparency misperceptions, which helps to explain the mystique of central bank speak

Shifts of criteria or neural timing? The assumptions underlying timing perception studies

In timing perception studies, the timing of one event is usually manipulated relative to another, and participants are asked to judge if the two events were synchronous, or to judge which of the two events occurred first. Responses are analyzed to determine a measure of central tendency, which is taken as an estimate of the timing at which the two events are perceptually synchronous. When these estimates do not coincide with physical synchrony, it is often assumed that the sensory signals are asynchronous, as though the transfer of information concerning one input has been accelerated or decelerated relative to the other. Here we show that, while this is a viable interpretation, it is equally plausible that such effects are driven by shifts in the criteria used to differentiate simultaneous from asynchronous inputs. Our analyses expose important ambiguities concerning the interpretation of simultaneity judgement data, which have hitherto been underappreciated

Full Reference Objective Quality Assessment for Reconstructed Background Images

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated Database: https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing (Email for permissions at: ashrotreasuedu