Applied Acoustics最新文献

Electroencephalography (EEG) is a vital tool for elucidating cerebral processes; however, it is inherently vulnerable to physiological interference, including cardiac rhythm, ocular movement, and muscular activity. To guarantee the reliability of essential neuronal data, it is imperative to implement efficacious denoising methodologies. This study compares the efficacy of two advanced blind source separation (BSS) techniques applied to EEG signals: variational mode decomposition-based BSS (VMD-BSS) and discrete wavelet transform-based BSS (DWT-BSS). The efficacy of these methods is rigorously assessed using performance metrics such as the Euclidean Distance (ED) and the Spearman Correlation Coefficient (SCC), which evaluate the precision of signal reconstruction and the correlation between the original and denoised signals, respectively. The findings indicate that both methods yield robust results, with minimal Euclidean distances of 704.04 for VMD-BSS and 703.64 for DWT-BSS, and a strong correlation coefficient of 0.82. The results demonstrate the effectiveness of the proposed techniques in removing artifacts while preserving essential neural information in EEG recordings. Furthermore, the proposed techniques are benchmarked against previous studies, considering factors such as signal properties, computational complexity, frequency localization, and flexibility. These findings highlight the importance of customized parameter selection tailored to the specific characteristics of EEG datasets and research objectives.

Picosecond ultrasonics (PU) combines the advantages of optical and acoustic measurements, and also provides nanoscale longitudinal resolution, making it the workhorse technique for in-line thickness measurement of opaque submicron films. In PU measurements of multilayer films, echo aliasing often occurs and leads to inaccurate thickness estimation based on straightforward time-domain analysis. This work proposes a model-based thickness estimation method for cases where some echoes are aliased, forming discrete echo-signal regions. The model used is lightweight and does not rely on reference signals obtained from standard specimens. Specifically, a theoretical model is developed to reflect the spectrum relationship between different echo-signal regions in one measurement curve, and is then used to fit the measured spectrum relationship to inversely extract thicknesses. Simulations are conducted and yield ways to reduce noise impact. Eventually, the proposed method is validated through PU measurements of submicron W/Al bilayer films, with estimation errors within 2.3%.

Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) are widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC extraction parameters on respiratory disease diagnosis. In this study, we address this gap by examining the effects of key parameters, namely the number of coefficients, frame length, and hop length between frames, on respiratory condition examination. Our investigation uses four datasets: the Cambridge COVID-19 Sound database, the Coswara dataset, the Saarbrücken Voice Disorders (SVD) database, and a TACTICAS dataset. The Support Vector Machine (SVM) is employed as the classifier, given its widespread adoption and efficacy. Our findings indicate that the accuracy of MFCC decreases as hop length increases, and the optimal number of coefficients is observed to be approximately 30. The performance of MFCC varies with frame length across the datasets: for the COVID-19 datasets (Cambridge COVID-19 Sound database and Coswara dataset), performance declines with longer frame lengths, while for the SVD dataset, performance improves with increasing frame length (from 50 ms to 500 ms). Furthermore, we investigate the optimized combination of these parameters and observe substantial enhancements in accuracy. Compared to the worst combination, the SVM model achieves an accuracy of 81.1%, 80.6%, and 71.7%, with improvements of 19.6%, 16.10%, and 14.90% for the Cambridge COVID-19 Sound database, the Coswara dataset, and the SVD dataset respectively. To validate the generalization of these findings, we employ the Long Short-Term Memory (LSTM) model as a validation model. Remarkably, the LSTM model also demonstrates improved accuracy of 14.12%, 10.10%, and 6.68% across the datasets when utilizing the optimal combination of parameters. The optimal parameters are validated using an external voice pathology dataset (TACTICAS dataset). The results demonstrate the generalization capabilities of the optimized parameters across various pathologies, machine-learning models, and languages.

To address the limitations of traditional honeycomb sandwich structures in attenuating mid to low-frequency sounds, particularly in configurations with minimal thickness and weight, this study introduces an innovative honeycomb acoustic metamaterial incorporating the traditional Chinese mortise-and-tenon joint. We systematically investigate the acoustic absorption characteristics of the modified honeycomb structure through theoretical analysis, empirical validation, and numerical simulations. Our experimental setup maintained consistent geometric parameters across all trials and demonstrated that the resonance frequency of the modified honeycomb structure decreased by 10 % relative to its conventional counterpart. We conducted detailed analyses on the influence of micropore positioning, tenon geometric dimensions, and micropore diameters on the acoustic performance. Notably, elongating the tenon from 2 mm to 6 mm resulted in a 15 % reduction in resonance frequency, whereas increasing the micropore-to-tenon distance from 0 mm to 4 mm led to a 30 % increase. The integration of the mortise-and-tenon joint significantly enhances the mid to low-frequency sound absorption performance of the honeycomb panels. This improvement is achieved while preserving the structural benefits of low panel thickness and shallow cavity depth, alongside simplified processing of micropores. Our findings elucidate a promising approach to augmenting the acoustic properties of lightweight structural materials, thereby extending their application potential in noise control engineering. This study not only contributes a novel perspective to the design and optimization of acoustic metamaterials but also highlights the potential for integrating traditional architectural techniques with modern material science to enhance noise control solutions.

Earplugs are widely used to prevent noise induced hearing loss. However, the discomforts they induce negatively impact their effectiveness by influencing their consistent and correct use. The occlusion effect discomfort is related to an increased perception of the bone-conducted part of physiological sounds (e.g., one’s own voice, breathing and chewing) when one’s earcanal is occluded. The discomfort experienced could be objectively estimated by calculating the objective occlusion effect, which is the difference between the tympanic sound pressure levels in the occluded and open earcanals when exposed to the same stimulation of a bone transducer. To avoid direct measurements on human participants, this work proposes an acoustical test fixture (ATF) for quantifying the objective occlusion effect. The proposed ATF employs an anatomically realistic truncated ear, incorporating soft tissues, cartilage, and bone components to replicate the outer ear bone conduction path crucial for occlusion effect assessments. It is shown that the proposed ATF can reproduce key effects observed in objective OE measurements on human participants: (i) significant OE at low frequencies, diminishing with increasing frequency, (ii) reduction of OE with greater insertion depths, and (iii) distinctions among various earplug types, particularly noticeable at deeper insertions compared to shallow ones. The proposed ATF can therefore be used to design in-ear devices with a reduced occlusion effect, leading to an improved experience for many users of hearing protectors, hearing aids, and earbuds. Additionally, a computationally efficient Finite Element Method-based virtual tester for the ATF is developed and validated. This virtual tester is employed to deepen the comprehension of the physical phenomena that underlie the observed vibroacoustic behavior of the proposed ATF. It also opens avenues for future research aimed at re-evaluating ATF design parameters and enhancing OE assessment.

A detailed overview of the hybrid anechoic wind tunnel at UTIAS is presented, highlighting its design and performance features. The findings demonstrated that the tunnel achieves a uniform flow with very low turbulence intensity, matching the performance of similar open-loop wind tunnel facilities. The anechoic chamber effectively reduces noise with a cutoff frequency of around 160 Hz, providing a suitable environment for a broad spectrum of aeroacoustic measurements. The versatility of the wind tunnel was illustrated through its application in various aerodynamic and aeroacoustic studies, showcasing examples such as the NACA 0012 airfoil, the multi-element 30P30N configuration, and finite-span airfoil investigations. Moreover, the facility's Overall Sound Pressure Level (OASPL) is on par with other prominent global aeroacoustic wind tunnels, indicating its competitive performance and utility in the field.

Bone conduction microphones (BCM) offer an interesting solution for speech recording in noisy environments. They provide enhanced ergonomics and reduced sensitivity to ambient noise compared to conventional aerial microphones. However, BCM exhibit inherent limitations in intelligibility and sound quality, even in quiet conditions, restricting their widespread adoption. To address these limitations, filtering techniques employing transfer functions between a conventional Air Conduction Microphones (ACM) and BCM have been explored. However, this study demonstrates the inadequacy of this approach due to the non-constant nature of the transfer function. An experiment involving ten subjects revealed that the transfer function between an ACM and a BCM, derived with a direct oral excitation, varies with a mouth opening. Additionally, a numerical investigation using finite element methods confirmed that the mouth opening significantly impacts the transfer function between the oral cavity sound pressure and an air conduction microphone but has negligible effect on the transfer function between the oral cavity sound pressure and a BCM. This paper try to explain the amplitude variation of bone-conducted speech and air-conducted speech depending on the vowel pronounced and highlights the inapplicability of Perceptual Evaluation of Speech Quality (PESQ) metrics for BCM. It opens avenues for signal processing techniques aimed at improving the quality and intelligibility of BCM-recorded speech.

Array signal processing is extensively utilized in the field of underwater acoustics (UWA). The majority of existing array signal processing algorithms require precise array position information to optimize their functionality. However, the intricate nature of the UWA environment introduces challenges such as the influence of water flow, which may result in deviations from the predetermined array positions. Consequently, this can amplify errors in array processing algorithms. Therefore, a high-precision array positioning method is needed to estimate the actual position of the array. The effectiveness of current array localization algorithms employing delay differential matching depends significantly on the accuracy of delay estimation and the formulation of the ambiguity function. In response to these crucial factors, this paper presents an algorithm for estimating array element positions that leverages an improved approach to delay estimation. Firstly, we propose the ρ-PHAT algorithm enhanced by the artificial fish swarm algorithm (AFSA-PHAT), significantly improving delay estimation accuracy, particularly in low signal-to-noise ratio (SNR) conditions. Compared to the traditional ρ-PHAT algorithm, this approach achieves a 3 dB increase in precision and a reduction in the root-mean-square error (RMSE). Additionally, a novel method is introduced for constructing the ambiguity function, which focuses on minimizing the acoustic complexity to encompass only direct and surface-reflected sounds. This improvement makes it particularly suitable for hydrophone arrays deployed near the sea surface. Computer simulations and experimental results validate that the algorithm, incorporating the aforementioned improvements, achieves enhanced accuracy in position estimation, reduced RMSE, and increased robustness.

Silent aeroengine nacelle highlights the demand for advanced acoustic liners providing broadband absorption. This paper studies the multi-degree-of-freedom (MDOF) septum liner that is a promising candidate. The steady-flow resistance of the septum as the necessary input parameter is measured on a steady-flow resistance tube herein, and an impedance prediction model is introduced and validated through the experimental measurement and the numerical simulation. Then, three liners with embedded septa of varied types, numbers and depths are designed, and the acoustic characteristics are analyzed in a wide frequency range under different incident sound pressure levels (SPL). It indicates that such liners can perform well in an ultra-broadband range up to 10000 Hz, and the impedance is relatively insensitive to the incident SPL. It is worth noting that, achieving broadband absorption for such liners is influenced by the coupling effect between zeros of the reflection coefficient, which correspond to the resonance frequencies of the liner. Moreover, the simulated results illustrate that the significant absorption for MDOF septum liners stems mainly from the sound energy dissipation at specific spatial positions depending on the frequency.