Applied Acoustics最新文献

Auditory steady-state response (ASSR) is a brain steady-state response induced by periodic sound signals, which can be used to build an auditory brain-computer interface (BCI), thereby providing a pathway for visually impaired patients to communicate with the outside world. Most of existing ASSR-based BCI studies use linear discriminant classifier (LDA) and spatial coherence to detect ASSRs. Therefore, there is an urgent need for efficient electroencephalogram (EEG) decoding methods to improve the performance of ASSR-based BCI systems. In this study, we elicited ASSRs using sinusoidal amplitude modulated (SAM) tones that simultaneously delivered different modulation frequencies (i.e., 37 Hz for the left channel and 43 Hz for the right channel). Subjects were asked to focus their attention on the auditory stimulation on one side according to the auditory cue. Filter bank common spatial pattern (FBCSP) algorithm was innovatively introduced to detect the ASSRs. Offline results showed that the brain region with strong ASSRs was the central forehead area, and when subjects paid attention to the auditory stimulation at 37 Hz or 43 Hz, the ASSR response of 37 Hz or 43 Hz on the corresponding side would be enhanced compared to the no attention condition. Online results obtained from twelve healthy subjects showed that the mean recognition accuracy of the proposed ASSR-based BCI system achieved a mean accuracy of 82.22 ± 3.11 %. Moreover, the present study further verified that weak auditory stimuli with low stimulus intensity (i.e., 40 dB SPL) could also be used to build ASSR-based BCIs, and achieved an online mean accuracy of 78.89 ± 2.54 %. These results verified that the FBCSP algorithm could be used for detecting ASSRs and the feasibility of the proposed ASSR-based BCI system, providing a great idea for building a high-speed ASSR-based BCI system.

We introduce a compact acoustic metamaterial designed for broadband soundproofing and ventilation within the low-frequency range. Our design features a transverse bilayer structure that consists of a central orifice surrounded by azimuthal labyrinthine channels. The interaction of transmitted sound waves from each region leads to Fano-like interference which enables to attenuate over 90 % of the incident sound energy across a broadband frequency range from 571 Hz to 1043 Hz. Moreover, the overall thickness of the acoustic metamaterial is just 30 mm, which is approximately 1/20 of the operating wavelength. The soundproofing efficacy of the designed model is validated through theoretical calculations, numerical simulations and experiments. The acoustic metamaterial proposed in this work may be applicable to various fields that demand simultaneous noise reduction and ventilation within limited spaces.

The aerodynamic whistle-based bat deterrent by Zeng and Sharma 2021, (Aerodynamic-whistles-based ultrasonic tone generators for bat deterrence, Physics of Fluids, 35, 2021) is numerically and experimentally analyzed for frequency modulation. Supply air pressure is modulated at various frequencies, amplitudes, and waveforms. At low frequencies (up to 100 Hz), the whistle exhibits quasi-steady behavior, and the radiated ultrasound follows the prescribed modulation. However, modulation at higher frequencies introduces multiple tones and distorts the modulation pattern. Measured farfield acoustic spectrograms reveal waveform skewness, likely due to nonlinear wave propagation in the conduit between the regulator and the whistle.

The axial defects in pipelines are hard to detect effectively with the conventional multi-mode total focusing method (MTFM). The inner and outer curved surfaces of pipelines induce beam divergence, reducing focusing performance and introducing imaging distortion and errors. In this paper, the MTFM employed for inspecting flat plates is modified in consideration of the influences of pipe curvature on the ray paths of direct, half-skip and full-skip mode waves. The corresponding travel times are recalculated and used for performing delay-and-sum (DAS) beamforming, achieving the profile reconstruction and quantitative detection of axial defects. The simulation and experiments were implemented on the carbon steel pipeline with 100 mm outer radius and 20 mm wall thickness. The phased array (PA) probe with 32 elements and 5 MHz central frequency and the 55° curved wedge were adopted to detect the inner-wall axial defects in pipeline. The profiles of the planar defects with a length of 5 mm and orientation angles of −45°∼45° were well reconstructed by modified MTFM, and the measurement errors of flaw lengths, orientation angles and tip depths were no more than 16.6 %, 5.1° and 4.0 % after correction, respectively. Further simulated and experimental results indicate that the internal axial defects can also be detected and quantified by modified MTFM. Finally, the influences of pipe curvature on modified MTFM are discussed by simulation.

Although the multi-channel active noise control (ANC) system based on the traditional clustered control strategy solves the problems of high algorithm complexity and fragile stability, the step size setting of each local controller relies on the trial-and-error method, which makes it difficult to trade-off the convergence speed and the steady-state error of the algorithm, and the secondary path estimation is susceptible to the interference of the dynamic environment. To solve the above problems, this study proposes an efficient multi-channel ANC system, which accurately estimates the secondary paths based on the deep learning prediction model and adopts a normalized-clustered control strategy to normalize the step size of the two-channel FxLMS algorithm of each local controller, which balances the system convergence speed and the steady-state error. A series of real-vehicle ANC experiments are conducted. The results show that under stationary conditions, the proposed control strategy control converges quickly and has better noise reduction performance than the traditional clustered control strategy. Under non-stationary conditions, after normalizing the step size of the local controller, the proposed control strategy can better balance the steady-state error and the convergence speed of the control strategy and improve the noise reduction tracking ability. Finally, it is verified that the proposed deep learning method can accurately estimate the secondary path after changes and ensure the noise reduction performance of the proposed control strategy.

Passive radial velocity estimation based on a single hydrophone has a wide range of applications in engineering, and can be used in small underwater platforms such as submerged buoys and underwater gliders. The conventional passive radial velocity estimation method based on a single hydrophone has poor performance due to the influence of noise. In this paper, the time delay coherence coefficient (TDCC) of passive target line spectrum is derived based on a single hydrophone. Further, the radial velocity can be obtained by the time delay required by the phase change of TDCC for one period. Based on the characteristics that the line spectrum phase is stable and the noise phase is random, coherent averaging (CA) is applied to suppress noise. The use of CA greatly reduces the influence of noise on TDCC and efficiently improves the accuracy of radial velocity estimation. The performance and system error of the proposed CA-TDCC method are analyzed through a series of simulations. Finally, the CA-TDCC method is used to process SwellEx-96 data, and the relative error of radial velocity estimation is less than 10%, which verifies the effectiveness of this method in practical applications.

Abbreviation: CA, Coherent averaging; SNRs, Signal-to-noise ratios; TDCC, Time delay coherence coefficient; TMA, Target motion analysis.

In most cases, the operation of underwater acoustic sensor networks (UASNs) relies on accurate sensor location information. However, UASNs present more challenges than terrestrial sensor networks, such as the propagation speed variation with depth (i.e., stratification effect), asynchronous clock, node mobility, etc. In this paper, an efficient method of asynchronous localization is proposed by taking into account the stratification effect and node mobility. Firstly, a network is constructed that consists of surface buoys, AUVs, and target sensors. Secondly, an iterative least squares (LS) method is developed to locate AUV by establishing the relationship between propagation delay and location estimation. Thirdly, the passive mobility velocity of AUV is used to develop and solve the mobility model of the target sensors. Then, with the support of AUV, an asynchronous localization method is developed to estimate the target position, which improves the localization accuracy through motion and stratification compensation strategies. Moreover, the proposed method is validated through Cramer Rao lower bound (CRLB) analysis. Finally, simulation is performed to show that the proposed method is more efficient in reducing the estimation errors.

In this work, we propose a bifunctional acoustic lossy coupler for broadband power splitting and absorption. Following the three-level system in quantum physics, the three-waveguide (WG) acoustic system is proposed to obtain efficient energy transfer behaviors. Given the agreement in form between Schr $\ddot{o}$ dinger equation and coupled mode equation, the time-dependent external fields are mapped into space-varying coupling actions between adjacent WGs. The propagation paths of the incident waves can be predicted by Schr $\ddot{o}$ dinger-like equation, which are verified by numerical simulations and experimental measurements. Owing to the different responses to lossy medium in WG B for WG A incidence and WG C incidence, the acoustic coupler can be considered as a broadband power splitter and muffler by selecting different input ports. Meanwhile, according to the reversibility of acoustic path, a switchable acoustic logic gate with functions of “OR” and “XOR” is constructed as well by taking advantage of the lossless adiabatic coupler, and the function can be controlled conveniently by reversing the phase of input waves from WG A or C. Our work provides a new scheme for the design of bifunctional acoustic coupler, which may have a profound impact on the development of non-resonance acoustic high-performance devices.

The acoustic quality of the new Teatro Argentino de la Plata received various unfavourable comments from musicians, critics and the general audience after its inauguration in 1999. The lack of bass frequencies, low sensation of envelopment, a flat sound—i.e., a sound that seems to come only from the front and does not surround the listener—and a strong seat-dip on the main floor were highlighted. After carrying out acoustical measurements and developing a digital model of the hall, the analysis showed, on the main floor level, a deficit of acoustical energy in the first 100 ms after the direct sound and insufficient lateral energy. To correct those problems a new acoustical canopy, made with a distributed array of rectangular panels, was placed over the pit. The situation improved substantially, but there was still a minor seat-dip in the stalls. A restauration was undertaken in 2016, which included the replacement of the seats, and it was seen as an opportunity to further fix the residual seat-dip problem. This paper briefly describes the action of the new canopy related to the seat-dip and the criteria for choosing the new seat model.

The number of sample snapshots of array signals directly affects the performance of direction of arrival (DOA) estimation methods, and smaller snapshots often cannot represent all the features of array signals. However, in practical applications, owing to short-time abrupt changes, low intensity, large noise interference, and other factors of the target signal, the acoustic vector array sometimes cannot obtain sufficient signal data, making it difficult to achieve accurate DOA estimation. Therefore, this study proposes a transfer-learning-based DOA estimation method for acoustic vector arrays. This method extracts the spatial–temporal features of existing signal data by constructing a pre-trained network model based on a convolutional neural network (CNN) and long short-term memory (LSTM), and transfers the trained model to scenes with limited snapshot data through model fine-tuning, achieving the goal of improving the DOA estimation accuracy under a small number of snapshots. Simulation experiments show that the accuracy and RMSE of the proposed DOA estimation method are superior to those of traditional methods when only 1% of the target data are used. This indicates that the pre-training model based on LSTM and CNN can preserve the effective information of signal data and provides a new solution for the real-time prediction of acoustic vector arrays in scenes with a limited number of snapshots through transfer learning.