When an undersea vehicle is in motion, the propeller generates significant low-frequency line spectrum radiated noise. To verify the control effectiveness of different active control strategies on the line spectrum radiated noise, an active control experiment on the radiated sound fields was conducted for the first time on a real small-scale propeller. An underwater test platform, including a propeller, sensors, secondary force actuators, secondary sound sources, and a control system, was constructed for active noise control(ANC), active vibration control(AVC), and active noise and vibration control(ANVC) tests, respectively. By altering the working conditions of the propeller, the suppression effects of different control strategies on the radiated noise of the underwater propeller were tested. The results show that ANC has a significant and stable effect on suppressing the low-frequency line spectrum noise of the propeller, and performs best under high rotational speed working conditions; AVC has limitations and local control may lead to sound field reconstruction; ANVC shows potential for collaborative control in some working conditions, while the control strategy and collaborative mechanism need to be further optimized. The research results provide an experimental basis and technical support for the engineering active control of underwater propeller radiated noise.

Tunnels play a crucial role in transportation infrastructure, and ensuring the safety of tunnel lining structures is critical for vehicles passing through. Acoustic detection of tunnel lining conditions is a viable approach to assessing structural integrity. In this study, acoustic signals are collected by striking the tunnel lining surface with a small hammer, and sound samples are generated in different reverberant environments to simulate tunnel conditions. A novel multi-channel cochleagram (MCCG) feature based on the multi-resolution cochleagram (MRCG) is introduced, which achieves data compression in reverberant environments. A convolutional network classification model based on the Convolutional Block Attention Module (CBAM), termed CBAM-SCNN3, is developed to classify hollow and solid sound samples under different reverberation. In the proposed method, the MCCG feature compresses and extracts multi-channel data, while the CBAM module adjusts weights of channels and spatial features. The depthwise separable network extracts features from MCCG in different directions. Experimental results show that the proposed method significantly outperform traditional features and exhibit improved robustness in reverberant environments while reducing network complexity. The average recognition accuracy over all reverberant environments is 98.4%, and the computational and parameter loads are reduced by 76.9% and 76.3%, respectively. Source code: https://github.com/EthanWu99/CBAM-SCNN3

Honeycomb structures have wide applications in the aerospace field due to their excellent structural properties. However, achieving high-resolution in-service inspection imaging without couplant remains a challenge. In this paper, a high-resolution imaging method based on the low frequency localized resonance mode in honeycomb structures is proposed. By analyzing the displacement eigen-fields of the dispersion relation, it is revealed that the skin’s localized resonance mode is caused by the restriction of the bond joint. Due to the low frequency of this mode, it can be easily excited in the structure without couplant and can propagate over long distances. Experimental results demonstrate that when this mode is excited in the structure, the signal amplitude response difference can reach about 25 dB at the honeycomb hole and core-skin bond joint, and therefore the hexagon cell (with a side length of 4 mm and a core wall thickness of 0.1 mm) of the intact area can be clearly observed in the imaging result. In contrast, these differences disappear in the defective area due to core-skin debonding. Further experiments confirm that the proposed method can obtain a high-resolution image, which is comparable to the result of water immersion focusing ultrasonic C-scan at a center frequency of 20 MHz. This method can also be integrated with laser vibrometers to achieve non-contact, high-precision imaging over large areas, which is more suitable for in-service inspection.

Anomalous sound detection (ASD) plays a critical role in industrial monitoring and equipment health management, yet model generalization remains challenging due to limited data and complex acoustic environments. In this paper, we propose RASD-SR, a robust anomalous sound detection framework. Leveraging a two-stage semi-supervised pseudo-label training strategy combined with a teacher-student secondary pre-training scheme, RASD-SR effectively exploits information from both labeled and unlabeled data. In addition, an adaptive combined perturbation algorithm is applied to embedding representations from multiple networks to jointly optimize multi-model embeddings which significantly enhancing robustness and generalization. Systematic evaluation on the DCASE 2024 Task 2 dataset demonstrates that RASD-SR achieves state-of-the-art official scores of 69.43% on the development set and 67.70% on the additional training set, validating its effectiveness in complex acoustic environments.

Objective Hydro-Turbine Generator Units(HTGUs)require reliable early fault detection to maintain operational safety and reduce maintenance cost.Acoustic signals provide a non-intrusive and sensitive monitoring approach,but their use is limited by complex structural acoustics,strong background noise,and the scarcity of abnormal data.An unsupervised acoustic anomaly detection framework is presented,in which a large-scale pretrained audio model is integrated with density-based k-nearest neighbors estimation.This framework is designed to detect anomalies using only normal data and to maintain robustness and strong generalization across different operational conditions of HTGUs. Methods The framework performs unsupervised acoustic anomaly detection for HTGUs using only normal data.Time-domain signals are preprocessed with Z-score normalization and Fbank features,and random masking is applied to enhance robustness and generalization.A large-scale pretrained BEATs model is used as the feature encoder,and an Attentive Statistical Pooling module aggregates frame-level representations into discriminative segment-level embeddings by emphasizing informative frames.To improve class separability,an ArcFace loss replaces the conventional classification layer during training,and a warm-up learning rate strategy is adopted to ensure stable convergence.During inference,density-based k-nearest neighbors estimation is applied to the learned embeddings to detect acoustic anomalies. Results and Discussions The effectiveness of the proposed unsupervised acoustic anomaly detection framework for HTGUs is examined using data collected from eight real-world machines.As shown in Fig.7 and Table 2,large-scale pretrained audio representations show superior capability compared with traditional features in distinguishing abnormal sounds.With the FED-KE algorithm,the framework attains high accuracy across six metrics,with Hmean reaching 98.7%in the wind tunnel and exceeding 99.9%in the slip-ring environment,indicating strong robustness under complex industrial conditions.As shown in Table 4,ablation studies confirm the complementary effects of feature enhancement,ASP-based representation refinement,and density-based k-NN inference.The framework requires only normal data for training,reducing dependence on scarce fault labels and enhancing practical applicability.Remaining challenges include computational cost introduced by the pretrained model and the absence of multimodal fusion,which will be addressed in future work. Conclusions An unsupervised acoustic anomaly detection framework is proposed for HTGUs,addressing the scarcity of fault samples and the complexity of industrial acoustic environments.A pretrained large-scale audio foundation model is adopted and fine-tuned with turbine-specific strategies to improve the modeling of normal operational acoustics.During inference,a density-estimation-based k-NN mechanism is applied to detect abnormal patterns using only normal data.Experiments conducted on real-world hydropower station recordings show high detection accuracy and strong generalization across different operating conditions,exceeding conventional supervised approaches.The framework introduces foundation-model-based audio representation learning into the hydro-turbine domain,provides an efficient adaptation strategy tailored to turbine acoustics,and integrates a robust density-based anomaly scoring mechanism.These components jointly reduce dependence on labeled anomalies and support practical deployment for intelligent condition monitoring.Future work will examine model compression,such as knowledge distillation,to enable on-device deployment,and explore semi-/self-supervised learning and multimodal fusion to enhance robustness,scalability,and cross-station adaptability.

Predicting the flow-induced interior noise of curved thick plate-cavity systems presents theoretical and computational challenges. Classical thin-shell models may become insufficient when the thickness-to-radius ratio is no longer negligible, and evaluating the vibro-acoustic response under stochastic turbulent boundary layer (TBL) excitation requires computing oscillatory quadruple spatial integrals, leading to high computational burdens. To address these issues, this study establishes a three-dimensional vibro-acoustic framework based on 3D elasticity theory and the Rayleigh-Ritz method, utilizing Chebyshev polynomials and artificial boundary springs to accommodate general elastic restraints. A comparison with a Donnell shell formulation is also included to examine the influence of finite-thickness modeling. To overcome the computational bottleneck associated with the TBL excitation, an effective computation strategy is proposed. By separating the modal shape correlation function and truncating the integration region, this method reduces the computational costs while preserving the resolution of convective hydrodynamic loads. Parameter analyses reveal that boundary stiffness governs a fundamental transition in the dominant boundary motion from sliding to bending. Furthermore, 3D stress analysis shows that increased plate thickness enhances the intrinsic wavenumber filtering effect, resulting in stronger through-thickness attenuation of the convective turbulent loads compared with transmitted acoustic plane waves.

The space vector pulse width modulation (SVPWM) methods have been widely applied in the high-precision control of permanent magnet synchronous motors (PMSMs). This study investigates the high-frequency noise harmonic induced by SVPWM methods. The function model between the harmonic amplitude and switching frequency has been analytically established. Simulation and experimental results indicate that although the random SVPWM method disperses the sideband harmonic, the overall sound pressure level (SPL) of the PMSM remains high. Increasing the switching frequency can significantly reduce the sideband harmonic amplitude and acoustic noise. By applying the parallel power semiconductors into the voltage source inverter (VSI), the additional inverter loss induced by higher switching frequency can be reduced. This provides a theoretical reference for the selection of vector control methods and VSI topologies.

Extended-neck acoustic liners, featuring perforated panels with extended neck, present a promising solution for low-frequency noise control in high-bypass-ratio turbofan engines under a constrained thickness condition. However, the in-orifice flow dynamics of such liners under grazing flow and high-intensity sound excitation remain poorly understood. This study employs large-eddy simulations (LES) to compare traditional and extended-neck liners under grazing flow conditions (Mach number M=0.0 similar to 0.075) and incident sound pressure levels (SPL=130 dB). Key findings reveal that the acoustic wave forces the shear layer into the orifice, where it strongly interacts with downstream acoustic-induced vortices during inflow. The extended neck delays the shear-layer motion with respect to vortex generation, yielding more symmetric vortical structures than conventional liners. This symmetric suggests a more linear acoustic-flow coupling mechanism, as vortex shedding and shear-layer interactions govern noise attenuation. The results highlight the extended-neck liner's superior noise-suppression capability, attribute to its modified flow dynamics and vortex organization.

Conventional space vector pulse width modulation (CSVPWM) with the fixed switching frequency generates significant sideband harmonics in the three-phase voltage. Discrete random switching frequency SVPWM (DRSF-SVPWM) methods have been widely applied in motor control systems for the suppression of tone harmonic energy. To further reduce the amplitude of the high-frequency harmonic with a limited switching frequency variation range, this paper proposes a time-division subbands beta distribution random SVPWM (TSBDR-SVPWM) method. The overall frequency band of the switching frequency is equally divided into N subbands, and each fundamental cycle of the line voltage is segmented into 2*(N-1) equal time intervals. Additionally, within each time segment, the switching frequency is randomly selected from the corresponding subband and follows the optimal discrete beta distribution. The switching frequency harmonic energy in the line voltage spectrum spreads across multiple frequency subbands and discrete frequency components, thereby forming a more uniform power spectrum of the line voltage. Both simulation and experimental results validate that, compared with CSVPWM, the sideband harmonic amplitude is reduced by more than 8.5 dB across the entire range of speed and torque conditions in the TSBDR-SVPWM. Furthermore, with the same variation range of the switching frequency, the proposed method achieves the lowest switching frequency harmonic amplitude and flattest line voltage spectrum compared with several state-of-the-art random modulation methods.

This study presents a reconfigurable acoustic metamaterial consisting of stacked multiple absorption modules, which can achieve efficient broadband sound absorption in both enclosed space and ventilation system. Each absorption module is composed of four Helmholtz resonators with embedded tubes, forming a square-shape structure with an air channel in the middle of the module. A theoretical model based on the impedance transfer method and a simulation model based on the finite element method were developed to investigate the sound absorption performance and the underlying working mechanisms of the metamaterial. Through the coupled resonance effects, each module exhibits a wide operating frequency band, and the absorption frequency range can be adjusted by modifying the module's geometrical parameters. And by stacking multiple modules, the acoustic metamaterial achieves an absorption performance that covers the operating frequency range of each module. Additionally, it was found that the interlayer coupling effects of modules significantly enhance overall absorption performance due to stepped well design in the center of the metamaterial. Moreover, by reconfigure the absorption modules, the acoustic metamaterial can be easily customized to achieve the desired absorption spectra. To illustrate the design concept, a metamaterial consisted of five-layer absorption modular is presented, with an average absorption coefficient of 0.92 within 450-2000 Hz range. Furthermore, the proposed metamaterial can be applied in different scenarios, i.e., with open and cloesed end. In the case where a rigid backing is placed at the bottom, the metamaterial can be applied as a sound absorber and its absorption bandwidth can be broadened to 430-2600 Hz by incorporating a porous material liner within the stepped well. In another case where the bottom of the stepped well is left open, the metamaterial can be used as a ventilation barrier, allowing airflow circulation and suppressing up to 90 % of sound energy within the 650-2400 Hz range. Therefore, not only does the proposed acoustics metamaterial design realize structural reconfigurability, but it also suggests a robust solution to low frequency noise-control that caters to diverse noise reduction requirements.

Granular Activated Carbon (GAC) is attracting more attention recently due to its better sound absorption performance at low frequencies compared with traditional porous materials, e.g., fibrous material. GAC is a porous particle with hierarchical pores, to predict its sound absorption capability, a triple porosity model which accounts the sorption effect in micro-pores was introduced by Venegas and Umnova. In the model, it was assumed that there are three levels of pore in the GAC stack, i.e., macro-pore between particles, meso-pore on the particles and micro-pore attach to the meso-pore. In previous study, the meso- and micro-pore size is fitted from sound absorption measurement result. In the present work, GAC material parameters, such as micro-pore size and meso-pore size, were measured with a standard isotherm measurement. Then these parameters were used as the input in the GAC model and the absorption coefficient was calculated without parameter fitting process. It was found that with the measured particle parameters, the calculated sound absorption coefficient agreed well with the measured absorption coefficient following E1050 standard. However, the value of measured parameters is different than that from inverse fitted, even though their sound absorption calculation results are similar.

Acoustic absorbers based on single-order resonant mode of the resonators have been extensively investigated for low-frequency broadband absorption. However, they are usually limited to a certain range of working frequency bands and often do not work effectively in the high frequency range. Here, we present an acoustic metamaterial, perforated panel with tube bundles (PPTB) combined with coiled-up cavity, which effectively expands the operating bandwidth from low to high frequencies by utilizing the multi-order resonances. The PPTB ensures the efficient low-frequency absorption effect though the adjustment of tube diameter and length. The coiled-up prolongs the propagation path of sound waves, thereby facilitating the excitation of higher-order resonance modes at high frequencies. The multi-order resonances mechanism of the metamaterial is revealed thoroughly by theoretical calculations and finite element simulations. The results show that at the first-order peak, the energy dissipation mainly occurs in the tubes, while for the high-order peaks, the energy dissipation of coiled-up cavity gradually increases. Moreover, this work introduces the coupled-mode theory to determine the reasonably structural parameters, which enables to achieve the desired leakage and loss factors simultaneously, maintaining the higher muti-order absorption peaks in a wide frequency band and providing the wider single-order absorption peak in high frequency range. Utilizing multi-order resonances, a broadband metamaterial supporting an average absorption coefficient above 0.93 within 300 -3600 Hz and eliminating the tangible absorption dips is obtained, which is mutually verified by theory, simulation and experimentation. Owing to its lightweight, lesscomplicated structure, terrific acoustic and mechanical performance, this kind of metamaterial may have a broad application prospect in noise control engineering.

Tunnels are an essential part of modern transportation infrastructure and their structural health is of significant importance for traffic safety. The cavity of tunnel lining has a serious impact on traffic safety. In this paper, an acoustic-based detection method for assessing the integrity of tunnel lining is studied. The acoustic signal is sampled by tapping on the surface of the tunnel lining. A Particle Swarm Optimized Support Vector Machine (PSO-SVM) classification model is built based on Mel-scale Frequency Cepstral Coefficient (MFCC) feature to classify the cavity and the dense acoustic signals of tunnel linings. The two parameters of the SVM are optimized, and the convergence curves are presented. Experimental results show that the recognition accuracy of PSO-SVM achieves up to 94.7

Random pulse width modulation techniques are used in AC motors powered by two-level three-phase inverters, which cause a broadband spectrum of voltage, current, and electromagnetic force. The voltage distribution across a wide range of frequencies may increase the vibration and acoustic noise of motors. To solve this problem, this study proposes a selective noise suppression (SNS) method to eliminate voltage harmonics. The general formula of the SNS is derived. In this method, the switching frequency is constant. The pulse position is calculated by the duty cycle of the current switching cycle. Simulation and experimental results show that the method effectively create a spectrum gap at a specific frequency. This study provides a valuable reference for eliminating electromagnetic vibration and acoustic noise at resonant frequencies in motors.

This article presents an active acoustic excitation method for leak detection of buried gas pipelines based on cavity resonance reflection. The principles of gas leakage in pipelines are analyzed, including the gas passage model and the gas cavity model. The principle of Helmholtz resonator is employed to establish the cavity model. For the cavity model, the relationships between cavity resonance frequency, acoustic impedance, sound pressure amplification, and leakage damage size are derived. The resonant effect of the gas cavity on the acoustic signal is considered in this study to solve the problem that the echo signal after long distance propagation and reflection becomes very weak. Numerical simulations are conducted to demonstrate the relationships between acoustic reflection coefficient of the leak hole size, cavity volume, and pipe wall thickness. In order to verify the effectiveness of the proposed method, a pipeline experimental rig with a length of 100 m is constructed. Sound waves are generated by a speaker and reflected echoes are received by a microphone. The cavity resonance reflection and echo characteristics of different leak hole size, different transmitting acoustic frequency, and different cavity volume are analyzed. The empirical mode decomposition (EMD) algorithm is used to decompose and reconstruct the echo signals to eliminate the noise interference in the pipeline system. An echo time-distance conversion method is used to visualize the locations of the leak hole and welds. Experimental results show that the proposed method can effectively detect the leak holes and welds in the pipeline.

The space vector pulsewidth modulation (SVPWM) has been widely used in ac motors powered by the two-level three-phase inverters. However, significant switching frequency harmonics are generated in the power spectrum (PS) of the output line voltage. To suppress the sideband harmonics near the switching frequency and its integer multiples, a novel cost function using the variance of all higher order harmonics in the PS is established and an optimization method for the selection of the switching frequency is proposed. According to an optimization case study, the optimal switching frequency distribution for the harmonic diffusion is the beta distribution. Furthermore, this article is concerned with the optimization parameter selection, including the number of the discrete switching frequency and the shape parameters of beta distribution to improve the harmonic dispersion effect in the line voltage spectrum. Simulation and experimental results demonstrate that a significant effect of the sideband harmonic dispersion is achieved when the number of discrete switching frequency is 2 x 10(1)-5 x 10(3) and the beta distribution shape parameters are set as 0.09-0.21. This provides the theoretical basis for the selection of the switching frequency and hence reduces the harmonics near the switching frequency in motor drive systems.

An ultra-broadband composite sound absorber composed of perforated panel resonators with tube bundles (PPTB) and porous sound absorbing materials (PSAM) is designed. The PPTB is established by utilizing multiple resonators with different resonance frequencies to obtain continuous low-frequency broadband absorption. By introducing PSAM in a proper manner around PPTB, the surface impedance of the structure is matched with air over a broader frequency range. As a result, the composite absorber designed with PPTB and PSAM achieves ultra-broadband sound absorption performance through the coupling effects of low-frequency resonance absorption by PPTB and high-frequency energy dissipation by PSAM. The acoustic-electrical analogy model and the finite-element method are applied to analyze the sound absorption performance. The results show that the composite structure has an average absorption coefficient of 0.93 in the ultra-broadband frequency range of 400 Hz-10 kHz. Moreover, the influence of structural parameters on the sound absorption performance is discussed, and the coupling sound absorption efficiency of PPTB unit under different damping states is compared. The impedance tube measurements validate that the ultra-broadband composite structure exhibits remarkable sound absorption properties within the frequency range of 400 Hz-1600 Hz. In comparison with porous materials of the same thickness, this composite sound absorber significantly enhances its low-frequency absorption performance.

Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature parameter extraction algorithm-the energy-frequency cepstral coefficient, which uses wavelet packet decomposition to obtain energy distribution statistics in the frequency domain of the signal, and constructs a signal-dependent filter bank to achieve the cepstral coefficient extraction. Compared with the traditional Mel filter bank, this method can adaptively adjust the resolution of the filter bank according to the frequency characteristics of the classified samples. This allows for higher frequency resolution in regions where the energy distribution is concentrated. As a result, the extracted feature parameters achieve both dimensional compression and superior information retention. Experimental results show that the proposed energy-frequency cepstral coefficient feature outperforms the traditional Mel-frequency cepstral coefficient feature, resulting in a higher accuracy of tunnel lining detection. The convolutional neural network model achieves an accuracy of 99.2%, with a 78.9% reduction in error rate compared with the traditional Mel-frequency cepstral coefficient feature parameters. Additionally, a particle swarm optimization support vector machine model is trained to achieve an accuracy rate of 99.6% and an error rate reduction of 76.5%.

Conventional series-coupled or parallel-coupled sound absorbers can only work effectively in a fixed frequency range, of which the absorption spectra can not be allowed to divide. Here, a parallel-coupled hierarchical structure with two relatively independent frequency bands of sound absorption is proposed. The hierarchical structure is composed of two type subunits of embedding neck Helmholtz resonator (ENHR) and segmented neck Helmholtz resonator (SNHR). Sound energy in the high-frequency region is dissipated in the first layer by the multiple local resonances coupling of ENHRs. The second layer effectively captures and absorbs low-frequency energy through the coupling of multiple SNHRs with varying parameters. The comprehensive discussion on the effects of acoustic impedance and structural parameters highlights the superior capability of this structure in impedance modulation. To demonstrate its broadband absorption performance, a hierarchical structure consisting of 20 subunits is employed. Finally, a above 90% broadband quasi-perfect absorption over a frequency range from 450 Hz to 1750 Hz is achieved under a thickness of 48 mm. Furthermore, a dual-band modular design concept for frequency-selective absorption is presented based on the tunability and independence of the two absorption bands. Therefore, the proposed hierarchical structure achieves reconfigurability by replacing either the first or second layer, thereby breaking away from traditional parallel design methods and providing a practical approach to controlling noise sources whose spectral characteristics change with operating conditions.

In the sound-absorbing structures based on resonance, acoustic resistance and acoustic reactance are usually coupled together, which affects impedance matching to obtain good absorption performance at required frequencies. To address this problem, a perforated panel structure with segmented impedance-decoupling tube bundles (PP-IDTB) is proposed to realize the decoupling of acoustic resistance and acoustic reactance. Theoretical and simulated results show that by changing the diameter of the upper thin tubes, the acoustic resistance can be varied, while the acoustic reactance remains constant, and thus the bandwidth of the absorption peak can be adjusted. The acoustic reactance varies with the length of the lower thick tube, while the acoustic resistance remains almost unchanged, and thus the resonance frequency of the absorption peak can be shifted. Moreover, impedance decoupling can improve the impedance matching effect in a wide frequency range, further enhancing the broadband sound absorption performance. Based on this, a parallel-coupled structure consisting of 9 PP-IDTB units is proposed. For demonstrating broadband absorption, the designed structure with a deep subwavelength thickness achieves near perfect absorption within a wide frequency range from 300 to 400 Hz. The design concept of impedance decoupling contributes to construct sound absorbers with highly efficient low-frequency broadband absorption.