Applied Acoustics最新文献

In the actual working process, the source of vibration signal is not only the rotor itself, so the detected vibration signal will become complicated. This complex signal makes it difficult to accurately measure the existence of crack. In this paper, a novel method, which includes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and continuous wavelet transform (CWT), is proposed to analyze the cracked rotor-rolling bearing system. The CEEMDAN-CWT successfully separates the vibration signal of the rotor itself from the original signal and provides results similar to the simulation signal. At the speed below 2000 rpm, the 2X frequency difference between cracked rotor and healthy rotor in CEEMDAN-CWT spectrum is about 1, while the difference of FFT spectrum of original signal is about 0.6, which shows the superiority of the novel method in extracting rotor vibration signals from complex vibration signals.

The integration of a singular vector acoustic system onto an unmanned underwater platform can lead to achieving unambiguous direction finding across the entire space. To enhance the direction-finding capabilities of the single vector acoustic system for low noise target, a refined histogram algorithm utilizing coherent spectrum weighting is suggested. A comparative evaluation and analysis of the target azimuth estimation performance of the enhanced histogram algorithm, traditional frequency-weighted histogram algorithm, and energy-weighted histogram algorithm are carried out. Through computer simulations, it is observed that the three Direction of Arrival (DOA) algorithms exhibit comparable direction-finding capabilities for wideband signals, whereas for single-frequency signals, the improved histogram algorithm surpasses the two conventional algorithms in direction-finding accuracy. Specifically, at a signal-to-noise ratio (SNR) of −40 dB, the azimuth estimation root mean square error (RMSE) is approximately 2°. Findings from sea trials indicate that the improved histogram algorithm displays a narrower spectral peak width and robust resistance to noise interference, thereby substantiating the effectiveness of the enhanced histogram algorithm.

In recent years, most state-of-the-art approaches for spoofed speech detection have been based on convolutional neural networks (CNNs). Most neural networks, including CNNs, are trained in minibatch units, where all input data in each minibatch must have the same shape. Therefore, for minibatch training, each utterance is first either padded or truncated because utterances are variable-length sequences and thus cannot be directly fed into networks in minibatch units. However, modeling either a padded or truncated utterance, rather than the original one, makes it unfeasible to capture the entire context as is: padding could propagate even unwanted information, like artifacts, in the original utterance, and truncation inevitably loses some information. With these information distortions, model could get stuck in a suboptimal solution. To fill this gap, we proposeÚ a method for precise utterance-level modeling that enables minibatch-wise utterance-level modeling of variable-length utterances while minimizing the information distortions. The proposed method comprises sequence segmentation followed by segment aggregation. Sequence segmentation feeds variable-length utterances in the minibatch unit by decomposing each of them into fixed-length segments, which enables parallel processing of variable-length utterances without the uncertainty in input length. Segment aggregation plays a role in aggregating the segment embeddings by utterance to encode the entire information of each utterance. The experimental results of the evaluation trials of ASVspoof 2019 and 2021 indicate that the proposed method shows up to 84.9 % and 97.6 % relative equal error rate reductions on logical and physical access scenarios, respectively. Furthermore, the proposed method reduced the FLOPs for an epoch by 6 %.

To address the time difference of arrival (TDOA) estimation problem in the passive positioning system with wideband underwater motion sound sources and distributed hydrophones, a hidden Markov model-based (HMM) TDOA sequence estimation method is proposed in this paper. The method estimates the TDOA with multi-frame output of cross-correlated signals received on hydrophones. The transfer equation of the TDOA is established as a first-order hidden Markov process by analyzing the motion characteristics of the moving sound source and delays obtained from different hydrophones. Dynamic assignment of the HMM parameters is proposed to address the inconsistent change rate of the TDOA. We then achieve an HMM expression of the TDOA sequence by fitting the transfer equation and dynamic assignment of parameters into the HMM. Then, the Viterbi algorithm (VA) is applied to distinguish the optimal sequence of the TDOA among ambiguous estimations. To deal with the problem of data loss or unreliable issues caused by interferences, a data prediction algorithm which could produce possible time delays is added to VA to avoid the impact of outliers on the estimation results. By utilizing multi-frame processing, the proposed method reduces the signal-to-noise ratio (SNR) requirement of single frames since it does not require accurate estimations of TDOA for each frame. Moreover, the method adapts to a lower SNR, which has significant advantages in terms of whole sequence estimation compared with common methods. The results from the simulations and lake experiments validated the proposed TDOA sequence estimation method.

Fatigue crack detection is an important issue to ensure the safety of steel strands. The key to solve this problem is to extract the nonlinear response generated by fatigue cracks. In this paper, the nonlinear VAM method is used to detect the structural cracks. The structure with fatigue cracks is excited using low-frequency pumping and high-frequency probing, and the power spectrum analysis of the modulated signal is carried out. In view of the shortcomings of spectral analysis, a new method combining S-transform and bispectrum is proposed, which is called S-transform bispectrum. S transform contains the phase factor, which can retain the absolute phase characteristics of each frequency, and has good time–frequency multi-scale focusing performance. The bispectrum can suppress Gaussian noise, retain phase information, and quantitatively describe the quadratic phase coupling in the signal. Then the simulation and experiment of damaged straight rod, helical rod, and steel strands are carried out. The results show that the proposed method can effectively detect the nonlinear features by using the sideband peaks in the S-transform bispectrum three-dimensional plot, and the nonlinear features are important to identify the structure with damage. At the same time, in order to prove the ability of S-transform bispectrum, an S-transform bispectrum detector is used to verify it, which is superior to the spectrum in terms of its ability to localize modulation sidebands. The proposed S-transform bispectrum has a good application prospect and provides a new tool for structural damage detection.

This study aims to analyze the spatial distribution of heavy-weight impact sound transmitted to a receiving room in 59 households within a concrete residential building. The performance of buildings is generally regulated by single number quantities such as ${L\prime }_{\mathrm{iA},Fmax}$. To enhance performance, it is important to understand the effect of the impact sound pressure level at designated impact positions and microphones. Therefore, this study proposes a method to quantify the impac sound pressure level for determining ${L\prime }_{\mathrm{iA},Fmax}$ according to the impact and microphone positions. Analysis of the data revealed that significant similarities in frequency spectra were observed when generating impacts at two positions near the window, in addition to two at the area near the corridor, both of which have similar boundary conditions. The central impact position exhibited the highest contribution at 24.6%, while other positions had contributions of 18.2–19.5%. For the microphone positions, those located at the corners on either side of the window showed contributions of 22.8–23.7%, whereas the remaining positions demonstrated lower contributions of 17.3–18.5%. These results can serve as basic data for developing construction methods to reduce floor impact sound.

Narrowband surface acoustic wave (NSAW) method is a promising ultrasonic detection technique, with its laser-induced excitation technology offering the advantages of non-contact and flexible regulation. This paper proposes an NSAW excitation and modulation system based on the grating mask method to detect the nonlinear characteristics of the spectra caused by the variation in material surface properties. According to Doyer’s sharp line excitation theory, a strip line source array excitation model formed by superposition principle is established, and the effects of duty ratios of the masks on NSAW spectra amplitude characteristics are interpreted. An NSAW excitation and B-scan experimental system that can realize line source spacing changes is developed, and the effects of cylindrical lens height and masks with different duty ratios on NSAW spectra modulation are studied. The experimental results show that the amplitude ratios (the ratio of the double frequency amplitude to the second harmonic amplitude) are consistent with the results of the Doyer superimposed strip line source array excitation model in which the excitation light source energy is evenly distributed. The second-order nonlinear coefficients extracted from the experimental spectra can effectively characterize the surface properties of 6061 aluminum alloy at different annealing temperatures.

This paper summarises an approach to generating controllable pressure gradients within an open-jet configuration for aeroacoustics research. A novel open-jet pressure gradient test rig has been designed for the UNSW Anechoic Wind Tunnel with the help of RANS simulations. A range of pressure gradients is created by adjusting the inclination angle of the top plate to change the cross-sectional area along the streamwise direction gradually. The test model mounting point is located on the bottom plate adjacent to partially opened side walls to allow far-field noise measurements. A comprehensive characterisation of flow quality, pressure gradient parameters and acoustic data quality has been carried out using Particle Imaging Velocimetry (PIV), surface pressure taps, and a microphone array. The test rig produces near-uniform pressure gradient flows at the model mounting point, with momentum Reynolds numbers ($R{e}_{\theta }$) ranging from 3014 to 11853 and Clauser's pressure gradient parameters (β) from -0.24 to 1.66. The pressure gradients generated by this facility are approximately linear, approaching the model mounting point, and the boundary layer profiles compare favourably with those from conventional hard-walled enclosed pressure gradient wind tunnel facilities. Measurements of airfoil trailing-edge noise from this test rig compare well with classical semi-empirical model predictions. Simultaneous acoustic and flow measurements on a square finite wall-mounted cylinder showcase the capability of this facility for coupled acoustic-flow diagnosis.

Planetary gearboxes are vital in industrial production due to their large transmission ratios. Therefore, accurate fault diagnosis of planetary gearboxes is crucial. However, in industrial applications, the acoustic fault signals from two different adjacent planetary gearboxes may overlap and interfere with each other, resulting in a low Signal-to-Noise Ratio (SNR) for each acoustic fault source, which in turn prevents accurate fault diagnosis. In this context, the Multi-Task Learning-Temporal Convolutional Network (MTL-TCN) is proposed to simultaneously output the orientation of the acoustic sources as well as the fault type to solve the problem of interference between adjacent acoustic sources. A Spatial information based Multi-Task Channel Attention (SMTCA) mechanism is also proposed to solve the problem of acoustic signal overlapping by using the orientation information to calculate the weight of the acoustic signal channel and assigning it to the fault diagnostic task, which combines the sound field information into the separation of fault sources. Finally, a Multi-Channel Acoustic based diagnose System (MC-ABDS) is proposed, which contains a customized microphone array as well as a sound field information and fault feature information extraction method called Multi-Task Attention TCN (MTA-TCN). The system is validated by the data collected in the anechoic chamber, and it is effective for the acoustic overlapping and interference that occurs when two adjacent planetary gearboxes are operating. The orientation accuracy of the acoustic source reached 99.98 %and the diagnostic accuracy of the fault reached 92.08 %.

This study explores variations in teachers’ perception of indoor and outdoor soundscapes across different spaces within schools. A quantitative research design involved 452 teachers in the United Kingdom who participated in an online questionnaire. The questionnaire was distributed to UK teachers via random sampling on the Prolific platform, utilizing its customizable demographics for participant recruitment. A multi-method approach, combining closed- and open-ended questions, captured the multifaceted nature of soundscape perception. Participants reported on perceptions and experiences of school soundscapes in general areas, classrooms, hallways, dinner halls, playgrounds, and gyms. Findings reveal that schools are perceived as dynamic environments, characterized by a blend of chaos, engagement, and excitement, contradicting expectations of a calm atmosphere. Correlation analysis demonstrated weak associations between age and perceptions of the acoustic environment (r_s([452])=[0.116]), as well as gender (r_s([450])=[0.060]), teaching experience (r_s([450])=[0.117]), school type (r_s([450])=[− 0.109]), school location (r_s([450])=[0.098]), time spent in outdoor places (r_s([450])=[0.09]). A significant positive correlations were observed between wellbeing and the overall school soundscape (r_s([450])=[0.286]), indicating that as self-reported wellbeing increases, the perceived quality of the school soundscape tends to increase. Differences were seen in the soundscapes of playgrounds, dinner halls, gyms, hallways, and classrooms compared to the overall school soundscape. These distinctions highlight varying levels of engagement, comfort, intrusiveness, and privacy across different areas, emphasizing the multifaceted nature of sound perception within schools. The study shows teachers use sound in education for different reasons and methods, with perceived impacts on student learning and wellbeing. It suggests the possibility of enhancing the educational experience through tailored interventions targeting specific areas in schools based on their unique soundscapes.