1,721,025 research outputs found
Sound event detection in underground parking garage using convolutional neural network
No full textParking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours are uncrowded places, where user safety is guaranteed by company overseers. Due to the large size, ensuring adequate surveillance would require many operators to increase the costs of parking fees. To reduce costs, video surveillance systems are used, in which an operator monitors many areas. However, some activities are beyond the control of this technology. In this work, a procedure to identify sound events in an underground garage is developed. The aim of the work is to detect sounds identifying dangerous situations and to activate an automatic alert that draws the attention of surveillance in that area. To do this, the sounds of a parking sector were detected with the use of sound sensors. These sounds were analyzed by a sound detector based on convolutional neural networks. The procedure returned high accuracy in identifying a car crash in an underground parking area
Modelling sound absorption properties for recycled polyethylene terephthalate-based material using Gaussian regression
No full textPlastic is widely used all over the world and its production has been increasing continuously in recent years. But plastic presents significant problems about its end-of-life given its important environmental impact. These problems impose recycling policies which provide for the collection and recycling of plastic materials. In this work, the acoustic properties of a recycled polyethylene terephthalate-based material were analyzed. The material showed good sound-absorbing characteristics, especially at high frequencies. In addition, a numerical model based on the Gaussian regression was developed to simulate the sound absorption coefficient of the material. The model returned an R-Squared value of 0.97 demonstrating excellent performance
Modeling acoustic metamaterials based on reused buttons using data fitting with neural network
No full textMetamaterials are designed by arranging artificial structural elements according to periodic geometries to obtain advantageous and unusual properties when they are hit by waves. Initially designed to interact with electromagnetic waves, their use naturally extended to sound waves, proving to be particularly useful for the construction of containment and soundproofing systems in buildings. In this work, a new metamaterial has been developed with the use of a polyvinyl chloride membrane on which buttons have been glued. Two types of buttons were used, with different weights, placing them on the membrane according to a radial geometry. Each sample of metamaterial was subjected to sound absorption coefficient measurements using the impedance tube. Measurements were made using the samples by setting three configurations, creating a cavity with different thicknesses. The results of the measurements were subsequently used as input for training a simulation model based on artificial neural networks. The model showed an excellent generalization capacity, returning estimates of the acoustic absorption coefficient of the metamaterial very similar to the measured value. Subsequently, the model was used to perform a sensitivity analysis to evaluate the contribution of the various input variables on the returned output
Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
No full textAcoustic emission is a nondestructive control technique as it does not involve any input of energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously emitted by a material under stress due to irreversible phenomena such as damage, microcracking, degradation, and corrosion. It is a dynamic and passive-receptive technique that analyzes the ultrasonic pulses emitted by a crack when it is generated. This technique allows for an early diagnosis of incipient structural damage by capturing the precursor signals of the fracture. Recently, the scientific community is making extensive use of methodologies based on machine learning: the use of machine learning makes a machine capable of receiving a series of data, modifying the algorithms as they receive information on what they are processing. In this way, the machine can learn without being explicitly programmed, and this implies a huge use of data and an efficient algorithm to adapt. This review described the methodologies for the implementation of the acoustic emission (AE) technique in the evaluation of the conditions and in the monitoring of materials and structures. The latest research products were also analyzed in the development of new methodologies based on machine learning for the detection and localization of damage for the characterization of the fracture and the prediction of the failure mode. The work carried out highlighted the strong use of these methods, which confirms the extreme usefulness of these techniques in identifying structural damage in scenarios heavily contaminated by residual noise
Membrane-type acoustic metamaterial using cork sheets and attached masses based on reused materials
No full textTo contain the environmental impact of modern buildings it is necessary to design in a sustainable way, making efficient use of energy resources and raw materials. The reduction of waste requires a review of the policy of valorizing raw materials by resorting to recycling and reuse as much as possible. In this study, a new membrane-type acoustic metamaterial was developed using a recycled cork membrane and fixing to this membrane masses obtained by reusing thumbtacks and buttons. About 42 samples were packaged by attaching different combinations of masses to the cork membrane. The specimens thus obtained were used to measure the sound absorption coefficient by means of an impedance tube. To improve the acoustic properties of the material, the benefits of membrane resonance absorption and cavity resonance absorption were coupled, leaving a 50 mm cavity at the end of the tube. Subsequently, the measurements were used to train a regression tree-based sound absorption coefficient prediction model. The results obtained suggest using these structures for the acoustic correction of the rooms.(c) 2021 Elsevier Ltd. All rights reserved
Improving smart cities safety using sound events detection based on deep neural network algorithms
No full textIn recent years, security in urban areas has gradually assumed a central position, focusing increasing attention on citizens, institutions and political forces. Security problems have a different nature-to name a few, we can think of the problems deriving from citizens' mobility, then move on to microcrime, and end up with the ever-present risk of terrorism. Equipping a smart city with an infrastructure of sensors capable of alerting security managers about a possible risk becomes crucial for the safety of citizens. The use of unmanned aerial vehicles (UAVs) to manage citizens' needs is now widespread, to highlight the possible risks to public safety. These risks were then increased using these devices to carry out terrorist attacks in various places around the world. Detecting the presence of drones is not a simple procedure given the small size and the presence of only rotating parts. This study presents the results of studies carried out on the detection of the presence of UAVs in outdoor/indoor urban sound environments. For the detection of UAVs, sensors capable of measuring the sound emitted by UAVs and algorithms based on deep neural networks capable of identifying their spectral signature that were used. The results obtained suggest the adoption of this methodology for improving the safety of smart cities
Acoustic characterization of rooms using reverberation time estimation based on supervised learning algorithm
No full textNumerical simulation for the sound absorption properties of ceramic resonators
Full text linkThis work reports the results of experimental measurements of the sound absorption coefficient of ceramic materials using the principle of acoustic resonators. Subsequently, the values obtained from the measurements were used to train a simulation model of the acoustic behavior of the analyzed material based on artificial neural networks. The possible applications of sound-absorbing materials made with ceramic can derive from aesthetic or architectural needs or from functional needs, as ceramic is a fireproof material resistant to high temperatures. The results returned by the simulation model based on the artificial neural networks algorithm are particularly significant. This result suggests the adoption of this technology to find the finest possible configuration that allows the best sound absorption performance of the material
Machine learning-based algorithms to knowledge extraction from time series data: A review
Full text linkTo predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data
Video games noise exposure in teenagers and young adults
No full textMany teenagers’ free time is almost entirely devoted to video games. Unlike reality, in the virtual environment, adolescents feel themselves as protagonists by interacting with people and objects that are very far from the current living environments. Several authors in the literature have highlighted the risks associated with an intensive use of these technologies and the negative consequences for health. To make the gaming experience as exciting as possible, these applications are equipped with sound environments that stimulate attention and aggression. This study describes the noise exposure measurement activities for video game users. The damage caused by noise depends on both the acoustic power and the exposure time. For this reason, different noise exposure scenarios produced by video games have been simulated. The results show that the daily levels of exposure to noise are close to the limits imposed by the legislation. A game exceeds the lower exposure limit for two different time exposures. In the case of 4 h of exposure, the lower exposure limit is exceeded, although the other 4 h of rest have been passed in an environment with low background noise (46.0 dBA). The results suggest limiting the set sound level appropriately and to reduce users’ exposure times
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