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Perception of room mode damping from a musical perspective
(European Acoustics Association, 2024-01-17) Jun, David; Glorieux, Christ; Fišarová, Zuzana; Plášek, Josef; Rychtáriková, Monika
Undamped acoustic modes inside rooms - especially in small ones - are often responsible for a decreased acoustic quality, in particular when the rooms’ main function is ”critical listening” and/or ”playing musical instruments”. Mainly in the latter case, treatment with broadband absorption is not always efficient and yields either uneven or too short reverberation times. Rehearsal rooms are typical cases where the ”liveness” of the space needs to be guaranteed and putting a broadband absorber would make it ”too dry”.Rehearsal room design can be seen as a search for an optimal compromise between sound strength and reverberation time across the audible part of the spectrum. This contribution presents results from listening test experiments, in which the perception of modes was investigated, in particular with respect to what extent differences in axial room mode damping are audible.
Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods
(Public Library of Science, 2024-01-18) Pálková, Martina; Uhlík, Ondřej; Apeltauer, Tomáš
Machine learning methods and agent-based models enable the optimization of the operation of high capacity facilities. In this paper, we propose a method for automatically extracting and cleaning pedestrian traffic detector data for subsequent calibration of the ingress pedestrian model. The data was obtained from the waiting room traffic of a vaccination center. Walking speed distribution, the number of stops, the distribution of waiting times, and the locations of waiting points were extracted. Of the 9 machine learning algorithms, the random forest model achieved the highest accuracy in classifying valid data and noise. The proposed microscopic calibration allows for more accurate capacity assessment testing, procedural changes testing, and geometric modifications testing in parts of the facility adjacent to the calibrated parts. The results show that the proposed method achieves state-of-the-art performance on a violent-flows dataset. The proposed method has the potential to significantly improve the accuracy and efficiency of input model predictions and optimize the operation of high-capacity facilities.
Analysis of the use of behavioral data from virtual reality for calibration of agent-based evacuation models
(Elsevier, 2023-03-03) Juřík, Vojtěch; Uhlík, Ondřej; Snopková, Dajana; Kvarda, Ondřej; Apeltauer, Tomáš; Apeltauer, Jiří
Agent-based evacuation modeling represents an effective tool for making predictions about evacuation aspects of buildings such as evacuation times, congestions, and maximum safe building capacity. Collection of real behavioral data for calibrating agent-based evacuation models is time-consuming, costly, and completely impossible in the case of buildings in the design phase, where predictions about evacuation behavior are especially needed. In recent years evacuation experiments conducted in virtual reality (VR) have been frequently proposed in the literature as an effective tool for collecting data about human behavior. However, empirical studies which would assess validity of VR-based data for such purposes are still rare and considerably lacking in the agent-based evacuation modeling domain. This study explores opportunities that the VR behavioral data may bring for refining outputs of agent evacuation models. To this end, this study employed multiple input settings of agent-based evacuation models (ABEMs), including those based on the data gathered from the VR evacuation experiment that mapped out evacuation behaviors of individuals within the building. Calibration and evaluation of models was based on empirical data gathered from an original evacuation exercise conducted in a real building (N=35) and its virtual twin (N=38). This study found that the resulting predictions of single agent models using data collected in the VR environment after proposed corrections have the potential to better predict real-world evacuation behavior while offering desirable variance in the data outputs necessary for practical applications.
Seasonal Variability of Resuspension
(EDP Sciences, 2022-11-04) Linda, Jakub; Köbölová, Klaudia; Uhlík, Ondřej; Pospíšil, Jiří; Apeltauer, Tomáš
Particulate air pollution in cities is caused by a variety of sources. One of the less-studied contributors is wind-induced particle resuspension. As the wind speed increases, particles are removed from surfaces. These particles cause an increase in the total concentration in the air. It is known that particles of 10-2.5 m in size can be resuspended (PM10-2,5). Modern emission monitoring in cities also allows the monitoring of fine particles of 10, 2.5 and 1 m in size. The size fractions can then be sorted into PM10-2,5, PM2,5-1 and PM1. When breathed in, particles of different sizes cause various serious health risks. This paper focuses on the identification of the resuspension process of different particle size fractions by a data processing method. Data measured by automatic emission monitoring are used. It is confirmed that the concentration increase can be dominated by the fraction PM10-2,5. However, a concentration increase of fractions PM2,5-1 and PM1 is also evident with increasing wind speed. Although the increase in the PM1 fraction is smaller than PM10-2,5, it is more severe due to the respiratory deposition dose. The resuspension of particles of different fractions has different behaviours during the year. PM10-2,5 particles are dominantly resuspended in the summer months. In winter, on the other hand, the proportion of PM2.5-1 and PM1 particles increases, which may be related to the heating season
Reverberation measurement set for the interrupted noise method
(IOP Publishing, 2021-12-21) Jun, David; Nespěšný, Ondřej
This paper describes the methodology of measuring the sound absorption coefficient in a reverberation room. For its purpose, a measuring set was created combining professional with widely available hardware suitable for acoustic reverberation measurements. For the measurement itself, the interrupted noise method was chosen based on the standard ČSN EN ISO 354. Measurement and evaluation of data take place using scripts written in the Python programming language. An effective measuring set was developed both in terms of operation during measurement and in terms of subsequent data processing, accessing and graphical presentation.

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