The airframe noise of a 1:2 scaled aircraft model is investigated through a series of experiments in the hard walled closed test section of a large non-anechoic wind tunnel. A surface mounted phased microphone array is used to acquire the wall pressure fluctuations generated by the interaction of the wind tunnel flow and the propagating noise. Data from two measurement campaigns are analysed. The wind tunnel acoustic behaviour and the array performance are first assessed evaluating the results obtained from a reference sound source installed in the centre of the test section. The combination of different airflow speeds, array positions and sound sources are tested. The airframe noise is evaluated acquiring pressure fluctuations in different configurations. The main parameters varied during the tests are the flight configuration, the speed of the airflow, the incidence angle of the model and the position of the microphone array. Data are processed and then analysed in the frequency domain and using a conventional beamforming algorithm to retrieve the sound source maps over the regions of interest. Source maps are deconvolved with a CLEAN-SC routine and single source contributions determined through integration of auto-powers. The resulting spectra are finally scaled determining the exponents of the pertinent scaling laws.
When a car is driven at high speed, the total pressure fluctuations on the side window include convective pressure fluctuation and acoustic pressure fluctuation, which have different generation mechanisms, transmission characteristics and efficiency. In order to understand the aerodynamic noise transmission mechanisms through the side window and calculate the aerodynamic noise accurately inside the car, it is necessary to separate these two kinds of pressure fluctuations. Based on the existing full-size DrivAer model, the pellicular mode decomposition theory proposed in recent years was investigated. With this approach, the two pressure fluctuations from a compressible CFD calculation acting on the side window were separated successfully. Through comparison with the pressure data obtained with the improved wavenumber decomposition approach, the reliability and accuracy of the pellicular mode decomposition approach for solving the acoustic pressure fluctuation was validated. Moreover, in comparison with wavenumber decomposition, the pellicular mode decomposition can be applied easily to any surface with arbitrary shape, even the surface curved in 3D. Further advantage of this approach is the ability to reconstruct the pressure field of convective and acoustic components individually at any frequency, which can help to understand the characteristics of these two pressure components. Disadvantage is however the handling of a large number of numerically computed pellicular modes, which is limited in principle by the implementation of finite element method. As a consequence, a reliable convective pressure fluctuation is difficult to be acquired directly with this approach.
Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks.
In this study, we have explored the structural damage detection of truss structures using the state-of-the-art deep learning techniques. The surrogate models, deep neural networks, are used to train the knowledge of the patterns in the response of the undamaged and the damaged structures. The limited sensors are then used to collect the response from the truss structures. Most previous studies on structural damage detection by using the conventional neural networks have been limited by the lack of a technique that determines an optimum learning rate in the training process. Recent advances in deep learning techniques can provide a more suitable solution to the problems and the process of feature engineering. A 31-bar planar truss is considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output data sets, respectively. The results showed that, in all cases considered, the proposed surrogate model was possible to detect damaged states with very good accuracy.
Voice recognition technology can almost accurately recognize a user’s voice in the absence of background noise or when the noise levels are extremely low. However, voice recognition in the presence of background noise with various voice signals is complicated. The problem in the current voice separation scheme is that though voice separation is possible from mixed voice signals, a permutation problem occurs that renders it difficult to identify the desired signal among the separated signals. In this paper, we propose a driver voice separation method for autonomous vehicles. To solve the permutation problem, active-beacon-based driver sound separation (ABDSS) utilizing active sound is used to distinguish the driver’s sound. After recording the voice work, simulation was performed. In the simulation, the proposed method succeeded in separating and distinguishing the original voice signals from the mixed voice signals. In addition, the coherence, kurtosis, and skewness calculation were used to verify that the separated signals were correctly identified in the simulation. Therefore, the proposed method is simpler in terms of hardware configuration than the existing methods and it is suitable for in-vehicle voice separation systems as well.
Geometry of cabins, corridors, and materials used in railway passenger carriages can play important roles in acoustic performance inside the rail cars. In order to evaluate the noise level inside a rail car, a hybrid method based on the ray-tracing and image-source techniques is employed. A parametric model is constructed based on the field experiment performed in a typical coach cabin namely Fadak train in Iranian Railway. An Omni-directional sound source inside the passenger carriage is employed inside the cabin once for an unequipped cabin and once for operational one. Acoustic parameters such as reverberation time (T30), Center time (Ts), and the sound pressure level have been calculated and results are validated with experimental acoustic measurements. The model is experimentally calibrated and then acoustics performance of the passenger coach is enhanced by modifying the materials and absorption coefficients against two dominant sound sources in low to middle speed ranges. It is found the simple recommendation can remarkably enhance the acoustic performance over a broad range of frequencies.
Ancient Roman theatres represent a unique cultural heritage which is still used nowadays to host a variety of cultural activities and performances. Acoustic measurements show that the acoustics of these theatres does not always support the listening of music, which is common in modern performances. Although the acoustics of these theatres for modern use performances may be challenging, this cultural heritage offers a unique experience to the audience who can assist to a performance on the same seats once used by Romans. Nowadays, these unroofed theatres, whose walls behind and at the sides of the stage have rarely been restored, are often criticized for the weak sound strength. Moreover, these theatres are often exposed to urban modern background noise. The significant sound absorption due to the presence of the audience on the seating area (named cavea) and to the tapestries used in modern scenes makes challenging to support the acoustic reverberation in these theatres. In this paper, these aspects are described focusing on five ancient Roman theatres located in Southern Italy. The study reports acoustic measurements followed by virtual simulation results. As it was impossible to perform acoustic measurements with full audience occupancy, the presence of the audience was simulated using room acoustic software. The paper compares the acoustic characteristics of these five theatres considering their architectural characteristics, and discusses the role that the material used for their restoration had on their acoustics. Finally, some considerations about some reversible interventions to improve the acoustics of these ancient theatres for modern uses are reported.
DOI : 10.1016/j.apacoust.2020.107530 Anahtar Kelimeler :
Ancient theatres, Scena, cavea, Orchestra, Reverberation time, Audience
Cilt: 170 Sayı: 0 Sayfa: 107530-0 ISSN: 0003-682X
In this paper, a hybrid system consisting of three stages of feature extraction, dimensionality reduction, and feature classification is proposed for speech emotion recognition (SER). At feature extraction stage, an informationally-rich spectral-prosodic hybrid feature vector comprised of perceptual-spectral features; that is, mel-frequency cepstral coefficient (MFCC), perceptual linear prediction coefficient (PLPC), and perceptual minimum variance distortionless response (PMVDR) coefficient along with the prosodic feature of pitch (i.e. F0) are extracted for each frame. This feature vector is extracted from both speech signal and its glottal-waveform. The first and the second-order derivatives are then added to the above-mentioned vector to form a high-dimensional hybrid feature vector characterized by a large number of dimensions. At the next stage, i.e. dimensionality reduction, the dimensionality of this feature vector is reduced using a new proposed quantum-behaved particle swarm optimization (QPSO)-based approach. In this paper, a new QPSO algorithm (so-called, pQPSO) is presented that makes use of a truncated Laplace distribution (TLD) to generate new particles and thus to produce solutions (i.e. particles) that are all within a valid range of a problem (contrary to the standard QPSO). The contraction-expansion (CE) factor of the proposed pQPSO is also selected adaptively. Using the proposed QPSO algorithm, an optimal discriminative dimensionality reduction matrix (i.e. projection matrix) is estimated with emotion classification accuracy as a class-discriminative criterion. At the subsequent stage, vectors with reduced feature dimensionality are fed into a Gaussian elliptical basis function (GEBF)-type neural network classifier to detect their speech emotion. To accelerate the training phase of the GEBF classifier, a fast-scaled conjugate gradient (SCG) algorithm is correspondingly employed that does not need to adjust the learning rate. Finally, the proposed method is evaluated on three standard emotional speech databases of Berlin Database of Emotional Speech (EMODB), Surrey Audio-Visual Expressed Emotion (SAVEE), and Interactive Emotional Dyadic Motion Capture (IEMOCAP). The experimental results showed that the proposed method was more accurate than state-of-the-art ones in terms of detecting speech emotions.
Speech endpoint detection is an important part of modern speech information processing technology. The success of endpoint detection directly improves the performance and quality of speech coding, speech recognition, speech synthesis and human interaction. The robustness and detection accuracy of algorithms have always been hot topics for many scholars in the condition of low Signal-to-Noise Ratio (SNR) and complex noise. In this paper, we aim to provide an overview of the state-of-the-art in time domain, frequency domain and cepstrum domain for speech endpoint detection algorithms and to cast a glance at the challenges for future research.
DOI : 10.1016/j.apacoust.2019.107133 Anahtar Kelimeler :
Speech endpoint detection, Time domain, Frequency domain, Cepstrum domain, Neural network
Cilt: 160 Sayı: 0 Sayfa: 107133-0 ISSN: 0003-682X
In order to effectively control tire-pavement noise in long freeway tunnels, this study aims to determine suitable low-noise pavement texture. The main indicators evaluated include texture parameters, A-weighted sound pressure level, octave, sideway force coefficient and the actual unit cost of building single texture. Sound signals at the tire-pavement interface are detected by the OBSI system without the influence of other nearby sources, then acquired by Dewesoft system and sent to a laptop computer for post-processing on Coinv DESPET software platform. The continuous friction tester is used to measure sideway force coefficient on each pavement surface at same vehicle speed. All testing efforts are based on previous investigation sections and two specially constructed long freeway tunnel sections with various specific textures. Afterwards, the technique for order preference by similarity to ideal solution, is adopted for the final comprehensive evaluation. The study found that the freeway tunnel is a hostile acoustic environment. The tire-pavement noise inside the tunnel is about 20 dB(A) higher than the external normal sections, whether it is asphalt pavement or cement concrete pavement. In addition, the longitudinal equidistant groove with large center spacing of 25 cm, not only help eliminates the pumping noise but prevents the side sliding of vehicles, is considered to be an effective technique on the premise considering economic cost. Also note that, the section near the tunnel entrance belongs to the transition zone of tire-pavement noise and a bottom area of skid resistance. The transverse unequal spacing groove can yet be regarded as a suitable choice for this section and wet section inside the tunnel. What is more, an interesting finding is that there is no direct correlation between tire-pavement noise and sideway force coefficient, regardless of groove textures. This means that a quieter pavement texture can be obtained without hindering the skid resistance. In this sense, the findings of this study may provide a little practical reference to create a “quiet, safe,” surface texture suitable for the long freeway tunnels.
The hourly traffic volume at night is typically lighter than that during the day. However, the average speed and the rate of heavy vehicle tend to be higher at night. It is shown that the influence of the maximum A-weighted sound pressure level (LA,Fmax) is much more dominant in the night than during the day in the roadside areas of a highway. In order to predict and evaluate the traffic noise in the areas facing the road and to investigate the influence on the health effects of the residents living in such areas, it is indispensable accurately to grasp not only the equivalent continuous A-weighted sound pressure level but also the LA,Fmax and the dynamic properties of level fluctuation of noise.
DOI : 10.1016/j.apacoust.2019.107095 Anahtar Kelimeler :
LAeq, T, LA, Fmax, ΔLAmaxleq, Proportion of LA, Fmax, Frequency of heavy vehicles, Occurrence of heavy vehicles
Cilt: 160 Sayı: 0 Sayfa: 107095-0 ISSN: 0003-682X
Audio signal processing algorithms generally involves analysis of signal, extracting its properties, predicting its behaviour, recognizing if any pattern is present in the signal, and how a particular signal is correlated to another similar signals. Audio signal includes music, speech and environmental sounds. Over the last few decades, audio signal processing has grown significantly in terms of signal analysis and classification. And it has been proven that solutions of many existing issues can be solved by integrating the modern machine learning (ML) algorithms with the audio signal processing techniques. The performance of any ML algorithm depends on the features on which the training and testing is done. Hence feature extraction is one of the most vital part of a machine learning process. The aim of this study is to summarize the literature of the audio signal processing specially focusing on the feature extraction techniques. In this survey the temporal domain, frequency domain, cepstral domain, wavelet domain and time-frequency domain features are discussed in detail.
In this paper, the acoustic emission accompanying the formation of brittle cracks of finite length is investigated theoretically using the approach based on the application of Huygens principle for elastic solids. In the framework of this approach, the main input information required for calculations of acoustic emission spectra is the normal displacements of the crack edges as a function of frequency and wavenumber. Two simple approximate models defining this function are used in this paper for calculations of the acoustic emission spectra and directivity functions of a crack of finite length. The simplest model considers a crack that opens monotonously to its static value. The more refined model accounts for oscillations during crack opening and considers a crack of finite size as a resonator for symmetric modes of Rayleigh waves propagating along the crack edges and partly reflecting from the crack tips. Analytical solutions for generated acoustic emission spectra are obtained for both models and compared with each other. It is shown that resonant properties of a crack are responsible for the appearance of noticeable peaks in the frequency spectra of generated acoustic emission signals that can be used for evaluation of crack sizes. The obtained analytical results are illustrated by numerical calculations.
This study explored the effect of building façade on indoor transportation noise annoyance in terms of frequency spectrum and expectation for sound insulation. The stimuli for transportation noise (road, rail, and aircraft noise) consisted of the original sound recorded outdoors, and the indoor sounds involved sounds filtered by filters using frequency responses of the insulation of the façades. The first experiment, which was conducted to reveal the effect of the frequency spectrum, compared the relative annoyance due to indoor noise and outdoor noise, both presented at 45 and 65 dB (LAeq). The results showed that in most cases, the indoor noise with lower sound energy in the high-frequency range produced lower annoyance than the outdoor noise for equal sound levels. In the second experiment, the participants were asked to rate the annoyance due to transportation noises presented at sound levels ranging from 35 to 75 dB (LAeq) in a specific imagined hearing situation (indoors or outdoors). Contrary to the first experiment, the results showed that in most cases, the annoyance due to indoor noise in an imagined indoor hearing situation, where sound insulation of the façade is expected, was higher than that due to outdoor noise in an imagined outdoor hearing situation for equivalent sound levels. The results of the two laboratory experiments demonstrate that the building façade has influences on the indoor transportation noise annoyance due to the frequency spectrum and the expectation for sound insulation.
DOI : 10.1016/j.apacoust.2019.03.020 Anahtar Kelimeler :
Transportation noise, Annoyance, Frequency spectrum, Expectation for sound insulation
Cilt: 152 Sayı: 0 Sayfa: 21-30 ISSN: 0003-682X
The use of reconstructed noise signal as a primary reference signal is critical to active noise control in passenger ear-sides under high-speed conditions. A signal decomposition optimisation-based BP neural network for ear-side noise reconstruction (DBENR) algorithm is proposed. This algorithm contains the processes of signal decomposition optimisation (SDO), component fitness calculation (CFC) and ear-side noise reconstruction (ENR). The SDO method is divided into two steps. Firstly, multi-source noise signals are decomposed into a finite number of intrinsic mode function (IMF) components by empirical mode decomposition. Secondly, according to a proposed energy-extreme division method, the IMFs are reconstructed into three signal components, namely, high-, intermediate- and low-frequency components. CFC calculates the fitness of a component in each forward training process of a signal reconstruction BP network to obtain the optimal fitness value. The ENR model is obtained by regarding the optimal fitness values as the initial weights and the thresholds of the signal reconstruction BP network and training. The effectiveness of the proposed DBENR algorithm is validated using five noise signal sources collected from a vehicle. Compared with the signal reconstruction BP algorithm, the proposed algorithm is superior in reconstruction accuracy.
Multichannel speech enhancement has become increasingly popular in both academia and industry. Most existing algorithms work on the use of spectral, temporal or spatial correlations in observed noisy speech data. Nevertheless, little attention has been paid to joint exploitation of correlations in the time, space and frequency domain. In this paper, we propose to integrate joint time-space-frequency filtering into a unified framework by representing the short-time Fourier transform coefficients of observed multichannel speech data as a 3-dimensional complex-valued tensor. The spectral, temporal and spatial filters are iteratively updated to perform filtering on 3-dimensions of the tensor, respectively. A locally optimal solution can generally be obtained in just a few iterations. Experiments are conducted to test performances of the proposed framework on both the simulated and realistic acoustic systems. Experiment results on simulated acoustic systems show that the proposed framework outperforms some traditional multichannel speech enhancement algorithms in terms of objective measures. The performance in the real environment shows the proposed framework has an advantage over other tested algorithms in terms of subjective and objective measures. All the results show the proposed framework can achieve effective noise reduction with little distortion.
Due to improvements on combustion-engines and electric-engines for cars, tyre noise has become the prominent noise source at low and medium speeds. Models exist that simulate the noise produced by a rolling tyre, as do models that auralize different traffic situations from a basic data set. In this paper, an established model for tyre noise (SPERoN) is combined with an auralization tool. The combined model can predict the spectrum of the sound at 7.5 m, as well as reproduce the sound for a given listener position. The auralization uses a methodology where recorded sounds are converted to source signals for engine and tyre/road-interaction. These can be shaped by the spectra estimated in SPERoN and synthesized back into a pass-by signal. Psychoacoustic judgements were used to compare the modelled signals with recorded signals. To see how well the modelled signals match the real recorded signals for perception, two listening-tests were performed. The simulated and recorded signals were rated by pleasantness, loudness, roughness and sharpness using semantic differentials. It was found that responses for simulated and recorded signals correlate for all cases, but rankings could not be reproduced exactly. The model can be further improved to be more applicable for listening tests.
With double-leaf wall systems such as plasterboard walls, a high sound insulation can potentially be achieved with a relatively low weight. The accurate sound transmission analysis of this type of wall is challenging since the leafs are usually coupled to a common frame, and since the finite dimensions play a role at lower frequencies. Existing analytical models for sound insulation prediction account for the deformation of the wall in an approximate way, while detailed numerical models are computationally very demanding. In this work, a sound insulation prediction model that achieves a high prediction accuracy at a low computational cost is developed. The wall components that display low geometrical complexity, such as the wall leafs and the cavity, are modelled in an analytical way. Sound absorbents in the cavity are modelled as equivalent fluids. The metal studs, which have a highly deformable cross section, are modelled in full detail with finite elements. The sound fields in the sending and receiving rooms are modelled as diffuse; they are rigorously coupled to the deterministic wall model by employing a hybrid deterministic-statistical energy analysis framework. With the resulting room-wall-room model, the airborne sound insulation is predicted for a range of double-leaf plasterboard walls with single, double and triple plating and with different cavity depths. The obtained transmission losses are validated against the results of an extensive set of experimental tests. A very good agreement between predicted and measured transmission loss values is observed. The single number ratings for the airborne sound insulation for nearly all walls differ from the experimental values by 0–2 dB, which is close to the average experimental reproducibility. At the same time, the computational cost is more than three orders of magnitude lower than for recently proposed models of similar accuracy.
DOI : 10.1016/j.apacoust.2018.06.020 Anahtar Kelimeler :
Airborne sound insulation, Hybrid deterministic-statistical energy analysis, Double wall systems, Plasterboard walls, Flexible metal stud frame
Cilt: 141 Sayı: 0 Sayfa: 93-105 ISSN: 0003-682X
Cover Song Identification (CSI) is a task in Music Information Retrieval (MIR) that attempts to identify other versions of a song containing different structures, tonalities, and tempos, what brings several challenges to this task. Some of frameworks proposed to identify cover songs were evaluated through the Music Information Retrieval Evaluation eXchange (MIREX) competition that aims to assess algorithms for MIR tasks. Among the problems faced by researchers is the time complexity of the algorithms considered in the context of CSI, what limits or makes unfeasible their application in real-world scenarios. Considering this challenge, we present a time complexity evaluation of the two most popular frameworks presented and ranked at the MIREX competition. This assessment confirms both algorithms have the same worst-case scenario of O(n2), besides a significant difference in terms of their constants, which impact in overall processing time. Finally, this analysis can support researchers to improve algorithms for the CSI task, thus leading to more suitable frameworks for real-world scenarios.
DOI : 10.1016/j.apacoust.2020.107777 Anahtar Kelimeler :
Cover song identification, Time complexity, MIREX, Music information retrieval, Algorithms
Cilt: 175 Sayı: 0 Sayfa: 107777-0 ISSN: 0003-682X
The newly refurbished vertical tunnel (V-tunnel) at Delft University of Technology has been redesigned as a state-of-the-art facility for research in aeroacoustics (A-tunnel), as well as fundamental studies in laminar-turbulent transition and flow control. This manuscript focuses on the design and refurbishment aspects of the facility, including a description of the main modifications in the supporting structures and the drive system of the fan, with details of the flow conditioning and anechoic performance. A rigorous aeroacoustic and aerodynamic characterization of the facility is also presented, benchmarking the flow quality and acoustic performance of the new wind tunnel with respect to other aeroacoustic facilities across the world.
Noise is one the most common health hazards in the workplace. Noise-induced annoyance, as a measurable mental response, is one of the most significant adverse effects of noise. The level of annoyance from environmental noise is affected by several non-acoustic factors such as personality traits, hearing sensitivity, or attitude. The purpose of this study was to investigate the effect of hearing conservation education program on noise annoyance among workers of the textile industry. This is a pretest–posttest quasi-experimental study conducted upon the case group and the control group. 162 textile-industry workers exposed to excessive noise (77 individuals in the case group and 85 in the control group) were randomly selected and studied. The sound pressure level was first measured in the above-mentioned industry. Then, the degree of individuals noise annoyance was assessed using the ISO 15666 questionnaire and their knowledge, attitude, and practice using the KAP questionnaire1. The educational package was developed based on expert opinion and the initial assessment, and the questionnaires were completed after the educational intervention. The results revealed that an educational intervention would increase noise annoyance among the workers in the case group. It also affects the knowledge, attitude, and practice scores and improves them. Based on the results, older individuals experienced more noise annoyance, and practice improved with individuals age. The results of the study suggested that an educational intervention would lead to an increase in noise annoyance among workers by affecting their attitudes. It also improves knowledge, attitude, and practice scores. This survey is one of the few studies that focused exclusively on the impact of an educational intervention on noise annoyance. Therefore, the relationship between education and noise-induced annoyance in this article needs to be validated for other industrial settings with excessive noise, especially other textile factories.
Well-designed thin lightweight fabrics can effectively replace bulky porous materials traditionally used in sound absorption. In this paper, a method based on Johnson-Champoux-Allard (JCA) model and multiple regression is proposed to predict the sound absorption effect of woven fabrics. Detailed 3D geometric models are built for a set of woven samples. Multiple regression method is conducted to obtain the empirical formulae of the parameters required by the JCA model, such as geometric tortuosity, flow resistivity, and porosity. Experimental results from the impedance tube test agree well with the numerical predictions.
This paper proposed a type of lightweight multilayer honeycomb membrane-type acoustic metamaterials (AMs) and the transmission loss is studied experimentally. It is demonstrated that lightweight honeycomb sandwich panel AMs can break the mass law and obtain the great sound transmission loss at a light-weight. The mass per cell area of the metamaterial structure has 0.29 kg/m2 only and the total thickness of the structure is 4.2 mm, but the average sound transmission loss (STL) can achieve 17 dB. Experimental results from the impedance tube are presented to validate the conclusion and show that the STL peak frequency can be tuned to specific values by varying the combination of cell structures and membrane properties.
Room acoustic parameters of open-plan offices, e.g. ceiling, wall, screen, and furniture absorption, and screen height between workstations, affect strongly sound pressure level (SPL) of speech. The effect depends on the distance from the speaker. The aim of this experimental study was to examine the effect of ceiling, wall, and screen absorption, and screen height on the spatial decay of speech both at short and long distances from the speaker. Twenty-two experimental conditions were built in an open-plan office including 12 workstations. The conditions were combinations of ceiling absorption (2 levels), wall absorption (2 levels), screen absorption (2 levels), and screen height (4 levels). The spatial decay of A-weighted SPL of speech was measured according to ISO 3382-3 in workstations at distances from 2 to 9 m from the speaker. The A-weighted SPL of speech in the nearest workstation, at the distance of 2 m, varied between 53 and 61 dB, and at the distance of 4 m, between 46 and 60 dB. The spatial decay rate of A-weighted SPL of speech, D2,S, varied between 1.3 and 8.5 dB. The results confirmed that ceiling absorption is the most efficient way to increase spatial attenuation, but the attenuation effect also depends on the absorption of vertical surfaces like screens and walls, and the height of the screens. Our study is unique because it is the first experimental study conducted in a full-scale open-plan office, which investigates the impact of several sound absorbing surfaces and screen height simultaneously.
DOI : 10.1016/j.apacoust.2020.107340 Anahtar Kelimeler :
Open-plan offices, Room acoustics, Reverberation time, Spatial decay of speech, Room acoustic design, Sound propagation
Cilt: 166 Sayı: 0 Sayfa: 107340-0 ISSN: 0003-682X
The aim of this study is to provide a better comprehension and predict acoustic behavior of Yucca Gloriosa (YG) fiber using experimental and computational approaches. To this end, the FESEM images of fibrous samples with thickness of 15 and 30 mm were obtained and fiber diameter and orientation distribution were calculated using image analysis techniques. An in house Matlab-based code capable of generating fibrous structures was developed. The obtained parameters from experimental and morphological analysis of samples were implemented into the code to simulate 3D virtual structure of samples. Flow resistivity and tortuosity were calculated by numerically solving Stokes flow and Fick’s law through the 3D void space of generated structures, respectively. Different models able to predict the acoustic impedance and frequency-dependent sound absorption coefficient (SAC) of porous materials, including Delany and Bazley (D-B), Garai and Pompoli (G-P) and Johnson-Champoux-Allard (JCA) were analyzed and their suitability for prediction of acoustic behavior of YG fibers was evaluated. The results were compared with experimental data obtained using impedance tube method.
DOI : 10.1016/j.apacoust.2019.106999 Anahtar Kelimeler :
Sustainable green porous material, Sound absorption coefficient, Experimental and computational approaches, Yucca Gloriosa
Cilt: 157 Sayı: 0 Sayfa: 106999-0 ISSN: 0003-682X
Chinas high-speed railway system has made rapid progress in the past 10 years, but at the same time, it has brought noise pollution problems to the surrounding areas. The Baiyun Campus of Guangdong Polytechnic Normal University is located near the Wuhan-Guangzhou high-speed railway. To evaluate the impact of high-speed railway noise on student learning, the sound pressure levels, spectrum of frequencies, window attenuations and other parameters of the noise from the Wuhan-Guangzhou high-speed railway are measured. A maximum noise-level prediction model for high-speed railway noise is established based on the experimental data. The distribution of the maximum noise level in the school district is calculated used the established model and the effect of noise on student learning was discussed. The results show that the model prediction results are consistent with the experimental results, and the average absolute error is less than 2 dBA. Although the hourly equivalent noise level is in accordance with the environment quality standard of noise, the maximum noise level when the train is passing is still high, exceeding 65 dBA inside some classrooms and even exceeding 70 dBA in open space, which has a significant impact on student learning. The results of this study have important application value for assessing the impact of high-speed railway noise in China.