Acoustics Australia最新文献

An apparent disconnect exist in workplaces regarding identification of occupational hearing loss (OHL) and implementation of specific strategies to prevent progression of OHL, evident through continued high incidence of OHL. This scoping review aimed to identify evidence regarding targeted intervention used by industry, specifically to prevent the progression of OHL for workers. The scoping review was undertaken using the PRISMA-ScR methodology. Search terms were based on three broad categories, hearing loss, workplace, and intervention. Initially 1309 articles were identified for screening and 1,207 studies not meeting the criteria were excluded. Full text reviews of 102 articles were completed and a further 93 studies excluded. The scoping review produced nine studies which were quantitatively analysed. All interventions focused primarily on lower order controls, specifically administrative and personal protective equipment. Eight studies focused on awareness training, health monitoring, mandating hearing protection device use and fit testing, and using personal attenuation ratings as a predictor to OHL. Only one study mentioned isolation of workers from noise sources, and this was an interview study with workplace managers, not a specific intervention at a workplace. The result of the review highlights the lack of published literature on targeted interventions for workers with OHL. There is insufficient evidence to inform effective, impactful change in practice to prevent the progression of OHL. It is recommended that a system of collecting and assessing specific interventions and controls for workers with OHL be developed to better inform industry on strategies that will provide adequate protection for these workers.

Abnormal noise is a key factor affecting automobile ride comfort, and the localisation of abnormal noise sources is critical for noise control. Herein, a mathematical model is proposed for the localisation of automobile door rattle sources based on Lamb wave propagation theory, Morlet wavelet transform, and the principle of time-reversal focus positioning. The cause of vibration signal wave packet aliasing was explored through thin plate impact simulation, and narrow-band signal extraction was then determined. The influence of the initial generation time ({T}_{0}) of the rattle signal on the positioning imaging was obtained through a rattle noise source localisation test, and the signal imaging discrimination method at time ({T}_{0}) was proposed. Verification test results showed that the maximum positioning error of the automobile door rattle noise source was no greater than 3.2 cm, and the average positioning error was 2.01 cm, which confirmed the feasibility of the proposed method for locating the rattle noise source in the automobile door.

Monitoring the condition of divers during immersion is essential to ensuring their safety and planning permissible physical activity. Monitoring respiratory rates using a diver's respiratory sounds as parameters might be used for that purpose. This study was conducted with three types of diving apparatuses intended for civilian use: scuba, closed-circuit, and surface-supplied. Respiratory rates were investigated using wearable acoustic sensors installed in diving suit air cavities or remote hydrophones. The respiratory rate monitoring was also possible using a standard underwater voice communication system. Periodic breathing sounds are distinguishable in open-circuit scuba at a distance of 20 m, and a distance of 100–140 m with additional processing using wavelet transforms. The noise of inhalation and exhalation using closed-circuit breathing apparatus was monitored in the vicinity of the diver's respiratory tract. Respiratory sounds were distinguishable while using surface-supplied diving equipment (diver at the bottom, depth 8 m). The mean respiratory rate was 16.4 respiratory cycles per minute. The researchers could assess a diver's respiratory rate without invasive intervention in the breathing apparatus design. The data obtained were helpful for the remote monitoring of divers by the diving supervisor. Additionally, the data could be entered into a personal decompression computer for self-monitoring.

The cat family Felidae is one of the most successful carnivore lineages today. However, the study of acoustic communication between felids remains a challenge due to the lack of fossils, the limited availability of audio recordings because of their largely solitary and secretive behaviour, and the underdevelopment of computational models and methods needed to address these questions. This study attempts to develop a machine learning-based approach which can be used to identify acoustic features that distinguish felid call types and species from one another through the optimization of classification tasks on these call types and species. A felid call dataset was developed by extracting audio clips from diverse sources. Due to the limited availability of samples, this study focused on the Pantherinae subfamily. The audio clips were manually annotated for call type and species. Time–frequency features were then extracted from the dataset. Finally, several multi-class classification algorithms were applied to the resulting data for classifying species and call types. We found that duration, mean mel spectrogram, frequency range, and amplitude range were among the most distinguishing features for the classifications.

The sound absorption coefficient (SAC) of a composite multi-cell sound absorber in the low- and mid-frequency range is investigated by using experiment and numerical method. The composite sound absorber includes a MPP (micro-perforated panel) layer, a porous material layer, and an air cavity layer. The sandwich acoustic structure consists of an air cavity layer in between two MPP layers, which is backed by another air cavity layer. Maa’s model was used to describe the MPP layer, and the porous material layer was established by using Delany and Bazley’s model. The transfer matrix method (TMM) was used to calculate the surface impedance of each acoustic unit-cell of the composite multi-cell sound absorber, and the SAC of the composite multi-cell sound absorber was predicted by using the equivalent circuit method. Finite element (FE) models of the composite multi-cell sound absorbers are presented, and their sound absorption coefficient was measured by using an impedance tube method. The measurement data demonstrate the validity of the prediction results and are used to analyse various acoustic characteristics that depend on the structural parameters of each acoustic unit-cell. Furthermore, an optimal combination of the structural parameters of the composite multi-cell sound absorber can be realized by using the genetic algorithm (GA). The effect of the number of the acoustic unit-cells with different perforation ratios of the MPP layer and the depth of the air cavity layer is presented. They are the main design parameters that can control the SAC in different frequency ranges. The results also show that the SAC of the composite multi-cell sound absorber can be adjusted by increasing the number of the acoustic unit-cells and using the optimized design of the air cavity layer.

Experimental data were measured by Caldersmith over many years and during different stages of the construction of several violins. Unfortunately, it seems that these valuable data were never published. From the personal notes of Caldersmith that were printed (where resonant frequency, mass, thickness, instrument model, etc., are listed), a possible link between resonant frequencies of free plates and their finished instruments was investigated. However, there is no evidence to support that such a link could be strong; thus the rigidity of the rib assembly of the violins may play a more important role than generally considered.

This paper aims to study the current legislation in the Arab world regarding noise pollution and to compare its laws, in a second step, with those of the European Union to identify the differences and similarities between these two groups of countries' policies. The methodology of this study was based mainly on analytical and comparative approaches to process the data collected from 31 legislation texts from 22 Arab countries. The data were obtained primarily from the official and governmental websites of the various ministries and state institutions concerned with environmental laws, using Arabic and second-language web searches in each country. This study shows that legislators in the investigated countries are relatively engaged in addressing environmental issues, especially noise pollution. Except for Somalia, all Arab League member countries have developed laws and regulations on noise pollution. However, there are no guidelines on noise measurement except in Oman, Qatar, and Saudi Arabia. As well, the governments of these countries still need to develop guidelines for noise mapping. Therefore, it is believed that the legislation in these countries needs to be revised and stronger. In addition, no trace of the soundscape subject has been highlighted in the Arabic legislative texts. The comparative analysis indicates that most Arab laws do not comply with the international standards established by the European Union governments. According to this study, revising and updating Arab laws is necessary, and action strategies must be implemented to achieve sustainability goals.

Efficient and accurate prediction of ocean ambient noise spectrum level is very important to improve the detection capability of sonar equipment. The more factors taken into account, the more complex the establishment and application of the prediction model, which makes a low prediction efficiency. As a data-driven technology, neural networks have the ability to accurately predict the state of complex systems and can avoid complex physical modeling. In this study, a neural network model is built to predict ocean ambient noise spectrum level based on the data of sea water depth, temperature, salinity, sea surface wind speed and rainfall. The model is based on Genetic Algorithm (GA), Levenberg–Marquardt algorithm (LM) and Back Propagation (BP) neural network. The use of GA and LM makes the model combine the powerful mapping ability of neural network and the global search characteristic of GA. The model is used to predict the variation characteristics of spectral levels with frequency, depth, wind speed and rainfall rate, respectively. The predicted values are compared with the real values, for example, the RMSE values are all nearly below 2.04. The results show that the GA-LM-BP neural network prediction model is accurate and effective, and has flexible input factor scalability, which provides a paradigm framework for the establishment of multi-source and multi-factor spectral level prediction model of ocean ambient noise spectrum level based on deep learning.