Acoustics Australia最新文献

An experimental study on the effect of the speech characteristics of the signal-to-noise ratio (SNR) and speech rate on the intelligibility of announcements at railway stations was conducted using an artificial synthetic voice. Synthesized speech has recently been used in noisy environments both indoors and outdoors, but unlike its use in quiet environments, when the environment is noisy, the intelligibility of announcements may be reduced. For railway station announcements, while natural spoken voices are currently used for multilingual announcements and disaster response broadcasts, deep neural network synthesized voices, which use deep learning, have also been adopted. However, the effect of the acoustic characteristics such as the SNR and speech rate on the intelligibility of reproduced announcements in noisy public spaces such as railway stations has not yet been clarified from a practical viewpoint. In this paper, in order to determine the appropriate SNR and speech rate for synthetic voice announcements in railway stations, auditory impressions of announcements with varying SNR and speech rate were evaluated by participants using a five-point scale. Based on the evaluations, the appropriate conditions for the broadcast of synthetic voice announcements at the ticket gate and on the platform of a station are discussed.

The present study aimed to investigate the psychological and physiological effects of low-level meaningful artificial sounds on intellectual tasks. The psychological factors related to the degree of disturbance, concentration, and stress, as measured by subjective evaluation, were compared with the degree of physiological stress, as measured using salivary alpha-amylase activity. The results indicated that human speech and chewing sounds caused similar levels of psychological stress while performing intellectual tasks; however, chewing sounds caused comparatively less physiological stress than did human speech sounds. These findings suggest that depending on the sound stimulus, psychological and physiological stress characteristics may not coincide.

In recent years, noise pollution has been recognized as a significant environmental issue, and using sustainable materials as sound-absorbing building materials have drawn considerable attention. The influence of graded perforations on the acoustic characteristics of a 3D printed bio-degradable thick micro-perforated panel (MPP) having graded perforation and made of Poly Lactic Acid is presented. Thicker panels are considered owing to the mechanical strength required for practical wall applications. A fused deposition modeling based 3D printer is used to fabricate the MPPs with graded cylindrical perforations and different patterning of perforations. The sound absorption coefficient is measured using the impedance tube technique and compared with theoretical results obtained using an equivalent electro-acoustic model. Results revealed that for normal incidence, the absorption coefficient is only dependent on the overall perforation ratio of the panel, irrespective of the perforation gradation and patterning of perforation. This gives the freedom to distribute the perforation aesthetically for interior wall application. This work also proposes the effective perforation ratio approach to predict the sound absorption coefficient (SAC) of MPPs with graded perforation. For multi-thickness MPPs and MPPs with linearly graded thickness, improved sound absorption characteristics were observed both in terms of bandwidth of absorption and peak value of SAC compared to the conventional constant thickness MPPs.

This paper focuses on the voluteless centrifugal fan and proposes a method to reduce the aerodynamic noise of the fan. Through fluid and acoustic simulations, as well as experimental investigations, the fan is examined. The innovative approach involves the implementation of an anti-vortex ring structure at the impeller front disk of the centrifugal fan, and the influence of this structure on the fan's operational efficiency and noise characteristics is analyzed. Based on this analysis, the structural parameters are optimized to control the vorticity of the impeller, resulting in improved overall performance and noise reduction of the HW355 centrifugal fan. Furthermore, noise simulations are conducted using the Lighthill analogy method, which transforms the coupling of turbulent flow and noise into an acoustic analysis of equivalent sound sources in a quiescent medium. By analyzing the results in the frequency domain, noise directivity characteristics, sound pressure level distribution on the meridional plane, and conducting noise experiments, a comprehensive analysis of the acoustic characteristics of the voluteless centrifugal fan is conducted. The study verifies the optimization effects of the innovative anti-vortex ring structure on the aerodynamic noise of the voluteless centrifugal fan.

A robust and high-resolution deconvolution algorithm based on coordinate correction is developed for a compact conformal array on a three-dimensional (3D) moving platform such as underwater glider. First, the coordinate-correcting conventional beamforming is derived to directly estimate the azimuth of the long-range targets in the geodesic coordinate system for a 3D moving platform array. Then, we improve the extended Richardson–Lucy deconvolution (Ex-RL-dCv) beamforming algorithm utilizing coordinate correction to simplify the bearing estimation model from two-dimensional (2D) to one-dimensional (1D). The improved algorithm corrects the deconvolution point spread function (PSF) dictionary mismatch caused by the platform’s 3D motion, and has lower sidelobes compared with the Ex-RL-dCv algorithm without coordinate correction. Finally, simulations and results from field-trial data processing are presented. The results demonstrate that the improved Ex-RL-dCv beamforming based on coordinate correction can significantly suppress the high sidelobes caused by deconvolution model mismatch, and successfully realize the robust and high-resolution detection for targets using the compact conformal array on a 3D mobile platform.

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.