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The design, development, and acoustic characterization of the Psychoacoustic Listening Laboratory (PALILA) recently established at Delft University of Technology are presented in this manuscript. This laboratory comprises a soundproof room with a modular design and specialized audio equipment. Its primary objective is to conduct experimental investigations into the human perception of aeroacoustic noise sources, such as aircraft, drones, or wind turbines. Furthermore, PALILA is certainly suited for studying other sound sources (e.g. household appliances, ground vehicles, etc.). The manuscript outlines the fundamental characteristics of the facility (i.e. dimensions and materials). A thorough acoustic characterization is provided, including assessments of the background noise levels, reverberation time, free-field sound propagation, and transmission losses of the walls (with respect to the exterior). Overall, PALILA is deemed to be a suitable quiet environment to conduct high-quality psychoacoustic listening experiments., Aircraft Noise and Climate Effects

Complex acoustic systems typically present three-dimensional distributions of noise sources. Conventional acoustic imaging methods with planar microphone arrays are unsuitable for three-dimensional acoustic imaging, given the computational demands and the incapability to explicitly account for the presence of multiple sources. This paper proposes the use of global optimization methods to solve these shortcomings. An experiment with three incoherent speakers proved that this method can accurately determine the three-dimensional location and the respective sound level of each individual source. In addition, super-resolution is achieved beyond half the Rayleigh resolution limit., Aircraft Noise and Climate Effects

Complex test models in aeroacoustic experiments often present an arrangement of noise sources within a three–dimensional space. Planar microphone array normally have difficulties in separating sound sources in the direction normal to the array plane due to their poorer spatial resolution in this direction. This paper evaluates the benefits of combining asynchronous microphone array measurements for three–dimensional acoustic source. An experimental setup consisting of three out–of–plane speakers was considered. A planar microphone array was employed for the acoustic measurements in a baseline position and then displaced around the speakers to provide different points of view. The acoustic source maps obtained from each array position were combined using the geometric mean of their source autopowers. The performance of this approach in combination with the following acoustic imaging methods was investigated: conventional frequency domain beamforming (as baseline), functional beamforming, orthogonal beamforming, robust adaptive beamforming, CLEAN–SC, Richardson–Lucy deconvolution, and global optimization methods. For each case, the performance is evaluated in terms of accuracy in source position localization and spectral quantification in sound pressure level. In general, it was determined that combining additional views considerably improved the accuracy in terms of position localization (especially in the depth direction)., Aircraft Noise and Climate Effects, Control & Operations

Full-scale propeller measurements are useful to study the total noise contribution of a propeller-driven aircraft, including installation effects. Full-scale measurements under operational conditions also provide an accurate validation opportunity for propeller noise prediction models. These studies are, therefore, necessary to quantify and reduce the noise annoyance of propeller-driven aircraft. For propeller aircraft, rotating sources need to be considered. In this research, propeller noise is studied for a full-scale propeller using an acoustic microphone array. The acoustic imaging techniques used are Conventional Frequency-Domain or Time-Domain Beamforming for the stationary noise sources and the ROtating Source Identifier for the rotating noise sources. By applying these two acoustic imaging methods simultaneously, in addition to filtering in the spatial and frequency domain, also filtering in the source velocity domain can be exploited. These methods were applied to an engine run-up of a Pipistrel Velis Electro, the first fully-electric certified aircraft. This electric aircraft, placed on the ground, allows for an initial study on the present noise sources and their relative contributions. Ultimately, this information can be used to separate the measured spectrum into spectra of different noise components which in turn can be used for full-scale validation and improvement of propeller noise prediction models., Aircraft Noise and Climate Effects, Control & Operations

The contribution of the engine and the airframe to the total noise generated by an aircraft varies with the operating conditions. Semi-empirical models are able to account for such variations but require detailed engine and airframe data as input that is not readily available for most aircraft types and operations. This hinders the validation of these models through comparison between predictions and experimental data. This work investigates the sensitivity of semi-empirical models of engine and airframe noise to slight variations of the input data, representative of uncertainties in geometrical parameters and variability of the aircraft operating conditions during flyovers. In addition, the predictions are compared to measurements of A320, A330, and B777 landings and departures. This, together with the sensitivity analysis, indicates frequency regions where a mismatch between measurements and predictions exists. The deviation between predictions and measurements for landings can be partially explained by the underestimation of the sound pressure level of the higher harmonics of the fan. For takeoff, the models predict lower levels than measured. This is hypothesized to be associated with jet-installation noise, which is not accounted for in the semi-empirical models. The predicted spectra of the Airbus A320 and A330 were adjusted to account for jet installation noise, using levels available in the literature. This resulted in a better agreement between modeled and measured spectra at low frequencies., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Aircraft Noise and Climate Effects

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

IMPORTANCE: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. OBJECTIVE: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. DESIGN, SETTING, AND PARTICIPANTS: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. MAIN OUTCOMES AND MEASUREMENTS: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. RESULTS: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms