Applied Acoustics最新文献_第3页

Ultrasonic-based in-service monitoring of density for organic polymeric buried pipelines 基于超声波的有机聚合物埋地管道密度在役监测

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-18 DOI: 10.1016/j.apacoust.2024.110395

Yusen Wu , Jian Jing , Tao Lai , Peng Shen , Yongqi Hou , Feilong Mao , Yijia Liu , Huizhen Lu , Kai Zheng , Xiangdong Ma , Lei Sun , Hui Zhang

Non-metallic buried pipelines, particularly polyethylene (PE), are widely used in the transportation of oil and gas resources due to their low cost, corrosion resistance, and long lifespan. However, the aging of non-metallic buried pipelines is difficult to monitor in real-time during operation, which poses a significant threat to people’s life and property. Density, as an important physical property of materials, has significant potential in characterizing the aging of non-metallic materials. Nonetheless, there is little research on the density detection of curved structures based on ultrasound. Therefore, in this paper, a multi-layer heterogeneous acoustic transmission model is proposed to achieve in-service estimation of density for non-metallic buried pipelines. Building upon the transmission matrix theory, a model that is more closely aligned with the real monitoring environment was constructed, taking into account the curved surface structure of the pipeline. The model was validated through numerical simulations and experiments. The experimental results demonstrate that the model exhibits excellent performance in estimating the density of polyethylene pipelines, with an error within ±0.5 %. This study will serve as a potential powerful tool for in-service aging monitoring of non-metallic buried pipelines.

非金属埋地管道，尤其是聚乙烯（PE）管道，因其成本低、耐腐蚀、使用寿命长等特点，被广泛应用于石油和天然气资源的运输。然而，非金属埋地管道的老化情况在运行过程中难以实时监测，对人们的生命财产安全造成了极大威胁。密度作为材料的一项重要物理性质，在表征非金属材料老化方面具有巨大潜力。然而，基于超声波的曲面结构密度检测研究却很少。因此，本文提出了一种多层异质声波传输模型，以实现非金属埋地管道的在役密度估算。在传输矩阵理论的基础上，考虑到管道的曲面结构，构建了一个更贴近实际监测环境的模型。该模型通过数值模拟和实验进行了验证。实验结果表明，该模型在估算聚乙烯管道密度方面表现出色，误差在 ±0.5% 以内。这项研究将成为非金属埋地管道在役老化监测的潜在有力工具。

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引用次数: 0

Sound absorption mechanism and characteristic of a pressure-resistant sandwich structure supported by carbon fiber truss and embedded cavities in rubber core 由碳纤维桁架和橡胶芯嵌入空腔支撑的耐压夹层结构的吸音机理和特性

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-15 DOI: 10.1016/j.apacoust.2024.110386

Kangle Li , Liuwei Mao , Zihao Chen , Zhixin Huang , Zhiwei Zhou , Ying Li

This work presents a new underwater pressure-resistant sandwich structure (PRSS) that owns both well mechanical and acoustic properties. The two panels of PRSS are carbon fiber reinforced polymer (CFRP) and the core layer is made of the carbon fiber truss (CFT) and rubber matrix embedded with cavities. The test sample of PRSS is prepared and sound absorption coefficients are measured under various water pressures in the acoustic tube. Meanwhile, the finite element (FE) model of PRSS is established in the COMSOL to simulate its sound propagation behaviors in water. Gained experimental and numerical results have good agreements, which confirm the effectiveness of acoustic tube test and FE simulation. The experiment verifies the efficient sound absorption coefficient (≥0.7) of PRSS at the broadband frequency range (2800 Hz-10000 Hz) and also demonstrates its low sensitivity of sound absorption with respect to the change of pressure (0.1 MPa to 4 MPa). Then the sound absorption mechanism of PRSS is discussed through numerical analyses. It is found that there are two significant absorption peaks in the range of 500 Hz-10000 Hz. Besides, parameters effects on the two absorption peaks are characterized, revealing the critical “acoustic bridge” role of CFT in directing sound energy deeper into the structure and resulting in more dissipation.

这项研究提出了一种新型水下抗压夹层结构（PRSS），它具有良好的机械性能和声学性能。水下抗压夹层结构的两层面板为碳纤维增强聚合物（CFRP），芯层由碳纤维桁架（CFT）和嵌入空腔的橡胶基体组成。制备了 PRSS 的测试样品，并在声学管中测量了不同水压下的吸声系数。同时，在 COMSOL 中建立了 PRSS 的有限元 (FE) 模型，以模拟其在水中的声传播行为。获得的实验结果和数值结果具有良好的一致性，证实了声管测试和有限元模拟的有效性。实验验证了 PRSS 在宽带频率范围（2800 Hz-10000 Hz）内的高效吸声系数（≥0.7），也证明了其吸声对压力变化（0.1 MPa 至 4 MPa）的低敏感性。然后，通过数值分析讨论了 PRSS 的吸声机理。结果发现，在 500 Hz-10000 Hz 范围内有两个明显的吸声峰。此外，参数对这两个吸声峰的影响也得到了表征，揭示了 CFT 在将声能引向结构深处并导致更多耗散方面的关键 "声桥 "作用。

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引用次数: 0

Enhancing real-world far-field speech with supervised adversarial training 通过有监督的对抗训练增强真实世界远场语音能力

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-15 DOI: 10.1016/j.apacoust.2024.110407

Tong Lei , Qinwen Hu , Zhongshu Hou , Jing Lu

The generalization of speech enhancement models to real-world far-field speech encounters significant challenges, including low signal-to-noise ratio, high reverberation, and variable latency between far-field and near-field recordings. Additionally, using the non-ideal near-field recordings as the labeled desired output further reduces the effectiveness of commonly utilized predictive models. To tackle these challenges, we propose the Far-field to Near-field Speech Enhancement through Supervised Adversarial Training (FNSE-SAT) strategy. This approach leverages supervised adversarial learning via the Multi-Resolution Discriminator, leveraging diverse speech characteristics with different frequency resolutions. A temporal frame shift operation is also incorporated to mitigate alignment discrepancies observed in real-world data and its effectiveness is confirmed by counting the accuracy of Voice Activity Detection. Experimental validation in both causal and non-causal configurations demonstrates that FNSE-SAT significantly outperforms the state-of-the-art predictive model on real-world datasets. Furthermore, adopting the transfer learning strategy, where the model is initialized with a simulated dataset before fine-tuning with real-world data, strengthens the efficacy of FNSE-SAT, leading to superior outcomes. The results of character error rate show that FNSE-SAT generates fewer components that deviate from the textual content compared to the generative diffusion method. Reducing the Discriminator's resolution to a single version decreases the DNSMOS but has a slight effect on the character error rate.

将语音增强模型推广到真实世界的远场语音会遇到巨大挑战，包括低信噪比、高混响以及远场和近场录音之间的不同延迟。此外，使用非理想的近场录音作为标注的预期输出会进一步降低常用预测模型的有效性。为了应对这些挑战，我们提出了通过监督对抗训练（FNSE-SAT）进行远场到近场语音增强的策略。这种方法通过多分辨率判别器利用监督对抗学习，利用不同频率分辨率的各种语音特征。该方法还采用了时间帧移动操作，以减少在真实世界数据中观察到的对齐差异，并通过计算语音活动检测的准确性来证实其有效性。因果和非因果配置的实验验证表明，FNSE-SAT 在实际数据集上的表现明显优于最先进的预测模型。此外，FNSE-SAT 还采用了迁移学习策略，即先使用模拟数据集初始化模型，然后再使用真实数据进行微调。字符错误率的结果表明，与生成扩散法相比，FNSE-SAT 生成的偏离文本内容的成分更少。将判别器的分辨率降低到单一版本会降低 DNSMOS，但对字符错误率影响不大。

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引用次数: 0

Estimation of lung sound cycle span using spectro-temporal respiratory frequency evaluation 利用频谱-时间呼吸频率评估估算肺音周期跨度

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-15 DOI: 10.1016/j.apacoust.2024.110390

Irin Bandyopadhyaya, Premjeet Singh, Sudestna Nahak, Arnab Maity, Goutam Saha

The common lung disease diagnostics by pulmonologists involve manual thorax auscultation using stethoscopes. Despite years of experience, this method is susceptible to human errors, which an automated system can alleviate to a large extent. An important step towards computerized lung disease detection involves efficient extraction of inspiration-expiration phases of complete lung sound cycles (LSCs), which mainly suffer from inter-observer variability when a manual segmentation process is employed. This work proposes automated respiratory cycle extraction by utilizing a joint spectro-temporal respiratory frequency identification approach applied to the lung sound signal envelope. Considering the dynamics of LSC over time and corresponding frequencies, the energy distribution related to modulating spectral bands of respiration is quantified to further optimize the cycle extraction process. We also compare the performance of single and multi-channel lung sound signals for precise identification of lung sound modulation frequency. Results show that the cycle demarcation provided by the proposed LSC algorithm exhibits lower error when evaluated using the ground truth values.

肺科医生常用的肺部疾病诊断方法是使用听诊器进行人工胸廓听诊。尽管有多年的经验，但这种方法很容易出现人为误差，而自动化系统可以在很大程度上减少这种误差。实现计算机化肺部疾病检测的一个重要步骤是有效提取完整肺音周期（LSC）的吸气-呼气阶段，而在采用人工分割过程时，这主要受观察者之间差异的影响。本研究提出了一种适用于肺音信号包络的频谱-时间呼吸频率联合识别方法，从而实现自动呼吸周期提取。考虑到 LSC 随时间和相应频率的动态变化，量化了与呼吸调制频谱带相关的能量分布，以进一步优化周期提取过程。我们还比较了单通道和多通道肺音信号在精确识别肺音调制频率方面的性能。结果表明，在使用地面真实值进行评估时，所提出的 LSC 算法提供的周期划分误差较小。

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引用次数: 0

Speech emotion recognition using multi resolution Hilbert transform based spectral and entropy features 利用基于频谱和熵特征的多分辨率希尔伯特变换进行语音情感识别

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-15 DOI: 10.1016/j.apacoust.2024.110403

Siba Prasad Mishra, Pankaj Warule, Suman Deb

Speech emotion recognition (SER) is essential for addressing many personal and professional challenges in our everyday lives. The application of SER has shown potential in a number of domains, such as medical intervention, fortification of security systems, online marketing and educational platforms, personal communication, strengthening of devices and human interaction, and numerous other domains. Due to its extensive variety of applications, this subject has attracted the attention of several researchers for more than three decades. The performance of SER can be improved by adopting a suitable methodology for extracting the feature and using it to classify speech emotion. In our study, we used a novel technique known as the multi-resolution Hilbert transform (MRHT) method to extract the speech feature. We used the multi-resolution signal decomposition (MRSD) method to break down the speech signal frame (SSF) into a number of sub-frequency band signals, which are called modes or intrinsic mode functions (IMFs). Then, Hilbert transform (HT) is applied to each IMF signal to find the MRHT-based instantaneous amplitude (MRHIA) and MRHT-based instantaneous frequency (MRHIF) signal vectors. Features such as MRHT-based approximate entropy (MRHAE), MRHT-based permutation entropy (MRHPE), MRHT-based increment entropy (MRHIE), MRHT-based spectral entropy (MRHSE), and MRHT-based sample entropy (MRHSME) were calculated using each MRHIA and MRHIF signal vectors and the mel frequency cepstral coefficient (MFCC) feature extracted using the speech signals. The combinations of the proposed MRHT-based features (MRHAE + MRHPE + MRHIE + MRHSE + MRHSME) are known as the MRHT-based entropy feature (MRHEF). Subsequently, the MRHEF and MFCC features are used both alone and in conjunction to categorize speech emotion using a deep neural network (DNN) classifier. This results in emotion classification accuracies of 89.67%, 85.42%, and 83.48% for the EMO-DB, EMOVO, and SAVEE datasets, respectively. Comparing our experimental results with the other approaches, we found that the proposed feature combinations (MFCC + MRHEF) using a DNN classifier outperformed the state-of-the-art methods in SER.

语音情感识别（SER）对于解决我们日常生活中的许多个人和职业挑战至关重要。语音情感识别的应用已在许多领域显示出潜力，如医疗干预、强化安全系统、在线营销和教育平台、个人通信、加强设备和人机交互，以及许多其他领域。由于其应用范围广泛，三十多年来，这一课题吸引了众多研究人员的关注。通过采用合适的方法提取特征并用于语音情感分类，可以提高 SER 的性能。在我们的研究中，我们使用了一种称为多分辨率希尔伯特变换（MRHT）方法的新技术来提取语音特征。我们使用多分辨率信号分解（MRSD）方法将语音信号帧（SSF）分解成若干子频带信号，这些信号被称为模式或固有模式函数（IMF）。然后，对每个 IMF 信号进行希尔伯特变换（HT），以找到基于 MRHT 的瞬时振幅（MRHIA）和基于 MRHT 的瞬时频率（MRHIF）信号向量。利用每个 MRHIA 和 MRHIF 信号向量计算基于 MRHT 的近似熵 (MRHAE)、基于 MRHT 的置换熵 (MRHPE)、基于 MRHT 的增量熵 (MRHIE)、基于 MRHT 的频谱熵 (MRHSE) 和基于 MRHT 的样本熵 (MRHSME)，以及利用语音信号提取的麦尔频率倒谱系数 (MFCC) 特征。所提出的基于 MRHT 的特征组合（MRHAE + MRHPE + MRHIE + MRHSE + MRHSME）被称为基于 MRHT 的熵特征（MRHEF）。随后，利用深度神经网络 (DNN) 分类器，将 MRHEF 和 MFCC 特征单独或结合使用，对语音进行情感分类。这使得 EMO-DB、EMOVO 和 SAVEE 数据集的情感分类准确率分别达到 89.67%、85.42% 和 83.48%。将我们的实验结果与其他方法进行比较后发现，使用 DNN 分类器的拟议特征组合（MFCC + MRHEF）在 SER 中的表现优于最先进的方法。

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引用次数: 0

A wideband damage source localization method using enhanced virtual time reversal mirror technique and modal analysis with sparse acoustic emission array 利用增强型虚拟时间反转镜技术和稀疏声发射阵列模态分析的宽带损伤源定位方法

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-15 DOI: 10.1016/j.apacoust.2024.110405

Kangwei Wang, Yang Qian, Jie Xie, Jun Wang, Weiguo Huang

In order to reduce the possibility of damage or leakages in the pressure vessels, acoustic emission (AE) array is typically utilized in the structural health monitoring (SHM) and integrity assessment in view of its high sensitivity. However, accurately analyzing and localizing a wideband AE source in practice can be challenging due to the complex dispersion and multi-mode behavior of AE signals. In this study, an enhanced virtual time reversal mirror (VTRM) imaging method was proposed aiming to solve this situation. This method was comprised of Morlet wavelet transform, time reversal mirror technique and a multi-window energy ratio indicator, which can be used to reconstruct ultrasonic images and reveal the damage locations. The proposed method was testified on a steel plate, using standard Hsu-Nielsen source localization experiments with many different source locations and array layout configurations, hence guaranteeing its reliability and repeatability. In contrast with time difference of arrival and single-sensor model acoustic emission methods, it was validated to eliminate the noise disturbances and echo interferences, significantly reducing the risk of artifacts and obtaining a much higher stability and noise resistance than the compared methods. In conclusion, the feasibility of the proposed method in complex AE source localization has been sufficiently confirmed, and it has the potential to be further studied in more practical SHM applications in the future.

为了降低压力容器损坏或泄漏的可能性，声发射（AE）阵列因其高灵敏度通常被用于结构健康监测（SHM）和完整性评估。然而，由于声发射信号的复杂弥散和多模行为，在实践中准确分析和定位宽带声发射源可能具有挑战性。本研究提出了一种增强型虚拟时间反转镜（VTRM）成像方法，旨在解决这一问题。该方法由 Morlet 小波变换、时间反转镜技术和多窗口能量比指标组成，可用于重建超声波图像并揭示损伤位置。利用标准的 Hsu-Nielsen 声源定位实验在钢板上对所提出的方法进行了验证，实验中使用了多种不同的声源位置和阵列布局配置，从而保证了该方法的可靠性和可重复性。与到达时差法和单传感器模型声发射法相比，该方法经验证可消除噪声干扰和回声干扰，大大降低了产生伪影的风险，其稳定性和抗噪性也远高于同类方法。总之，所提方法在复杂声发射源定位中的可行性已得到充分证实，未来有可能在更多实际的 SHM 应用中得到进一步研究。

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引用次数: 0

The effect of time-varying characteristics of shallow-sea waveguides on low-frequency acoustic signal transmission 浅海波导的时变特性对低频声学信号传输的影响

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-15 DOI: 10.1016/j.apacoust.2024.110375

Lidong Huang , Zhuoyang Zou , Bin Wu , Wenrui Wang , Lingyun Ye

The time-varying characteristic of shallow sea channel plays one of the most important role in the exploration of ocean. However, there is still lack of estimation model to describe the time-varying characteristics of shallow water low-frequency acoustic channels. This paper proposes a low-frequency channel model based on wave equation and ray theory to estimate the time-varying characteristics of shallow sea. Firstly, for the actual characteristics of shallow sea, suitable boundary conditions are designed to solve the wave equation. Secondly, the constraint conditions of shallow sea wave equation are optimized by combining ray theory. Then, based on the physical characteristics of shallow sea, time-delay variation and phase variation are solved by shallow wave equation. Combining bellhop propagation model with ocean disturbance model, the simulation is designed to verify the time-varying characteristic model of low-frequency shallow sea in this paper. Finally, with different propagation distance measurement experiment of sea, estimation value of time-delay variation and phase variation are confirmed, which is significant to shallow sea channel.

浅海声道的时变特性在海洋探测中起着最重要的作用之一。然而，目前仍缺乏描述浅海低频声道时变特性的估算模型。本文提出了一种基于波方程和射线理论的低频声道模型来估计浅海的时变特性。首先，针对浅海的实际特点，设计了合适的边界条件来求解波方程。其次，结合射线理论优化浅海波浪方程的约束条件。然后，根据浅海的物理特性，用浅海波方程求解时延变化和相位变化。结合钟楼传播模型和海洋扰动模型，设计仿真验证本文提出的低频浅海时变特性模型。最后，通过对不同传播距离海域的测量实验，证实了时延变化和相位变化的估计值，这对浅海航道具有重要意义。

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引用次数: 0

Application of a priori knowledge-enhanced fuzzy clustering to acoustic emission-based damage identification of composite laminates 先验知识增强型模糊聚类在基于声发射的复合材料层压板损伤识别中的应用

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-15 DOI: 10.1016/j.apacoust.2024.110404

Weijie Ma , Fan Dong , Yazhi Li , Biao Li , Chunping Zhou

Acoustic emission (AE) technology has been widely used in the researches on composite damage identification. Nevertheless, traditional classification and clustering models usually ignore the underlying physical mechanisms of the complex failure process of composites, limiting the comprehensive understanding and analysis of damage mechanisms. In this paper, a Prior Knowledge-enhanced Fuzzy C-Means (PK-FCM) is developed and validated by open-hole tension and compression experiments on plain-weave glass fiber-cyanate composite laminates. The experiments successfully subdivided the multiple stages of composite damage development with the help of AE monitoring, fracture morphology observation and in-situ penetration flaw detection techniques. The PK-FCM algorithm uses the experimental prior knowledge to guide the clustering, and specifically solves the problem of damage accumulation and evolution characteristics of composite materials. By dynamically adjusting the membership matrix, the cumulative effect and evolution order between damage modes are accurately described. Compared with the traditional K-mean and fuzzy C-mean (FCM) clustering methods, PK-FCM reveals the core features of the damage evolution of composite materials, significantly improves the accuracy and prediction ability of damage analysis, significantly improving the reliability of damage identification and advancing our understanding on the damage mechanisms of composite materials.

声发射（AE）技术已广泛应用于复合材料损伤识别研究。然而，传统的分类和聚类模型通常会忽略复合材料复杂失效过程的内在物理机理，从而限制了对损伤机理的全面理解和分析。本文开发了一种先验知识增强型模糊 C-Means （PK-FCM），并通过对平纹玻璃纤维-氰酸酯复合材料层压板的开孔拉伸和压缩实验进行了验证。在 AE 监测、断口形态观察和原位渗透探伤技术的帮助下，实验成功地细分了复合材料损伤发展的多个阶段。PK-FCM 算法利用实验先验知识指导聚类，有针对性地解决了复合材料的损伤积累和演化特征问题。通过动态调整成员矩阵，准确描述了损伤模式间的累积效应和演化顺序。与传统的 K-均值和模糊 C-均值（FCM）聚类方法相比，PK-FCM 揭示了复合材料损伤演化的核心特征，显著提高了损伤分析的准确性和预测能力，大大提高了损伤识别的可靠性，推进了对复合材料损伤机理的认识。

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引用次数: 0

Robust fiber-optic microphone with modified dual-wavelength demodulation algorithm for low-frequency sound detection 采用改进的双波长解调算法进行低频声音检测的稳健型光纤传声器

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-14 DOI: 10.1016/j.apacoust.2024.110394

Xinyu Hu , Haibo Wang , Yan Yue , Lichao Zhang , Chenglong Zhang , Zhi-mei Qi

A fiber-optic Fabry-Perot (FP) microphone with a prestressed nickel diaphragm was prepared for low-frequency environmental noise detection. By using a modified dual-wavelength demodulation (MDWD) algorithm proposed in this work, the time-domain displacement of the microphone diaphragm is determined without the need to know the initial length of the FP cavity. The MDWD algorithm enables the microphone to operate at non-quadrature points and extends the microphone response beyond the linear region of the FP interferometer. The simulation results show that the time-domain displacement of the microphone diaphragm determined by the MDWD algorithm is accurate and the measurement error is less than 3.3 % in the case of the diaphragm displacement below 300 nm. The mechanical response of the fiber-optic FP microphone to 100 Hz sound waves at different pressures was measured using the MDWD algorithm and compared with that obtained by the broadband interferometric demodulation method. The two experimental results are in good agreement with each other, verifying the reliability of the MDWD algorithm. The mechanical sensitivity of the prepared microphone was measured to be 49.41 nm/Pa over the dynamic range of 0.14–3.16 Pa using the MDWD algorithm. Finally, the field detection of subway noise was performed using the MDWD-based fiber-optic FP microphone. The work demonstrated that the MDWD algorithm can significantly improve the performance of fiber-optic FP microphones for low-frequency sound detection.

我们制备了一种带有预应力镍膜片的光纤法布里-珀罗（FP）传声器，用于低频环境噪声检测。通过使用本文提出的改进型双波长解调（MDWD）算法，无需知道 FP 腔的初始长度，即可确定传声器振膜的时域位移。MDWD 算法使传声器能够在非正交点工作，并将传声器响应扩展到 FP 干涉仪的线性区域之外。模拟结果表明，MDWD 算法确定的传声器振膜时域位移是准确的，在振膜位移低于 300 nm 的情况下，测量误差小于 3.3%。使用 MDWD 算法测量了光纤 FP 传声器在不同压力下对 100 Hz 声波的机械响应，并与宽带干涉解调方法获得的响应进行了比较。两个实验结果非常吻合，验证了 MDWD 算法的可靠性。使用 MDWD 算法测得，在 0.14-3.16 Pa 的动态范围内，制备的传声器的机械灵敏度为 49.41 nm/Pa。最后，使用基于 MDWD 的光纤 FP 传声器对地铁噪声进行了现场检测。研究结果表明，MDWD 算法可显著提高光纤 FP 传声器在低频声音检测方面的性能。

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引用次数: 0

Sound propagation throughout the orchestra. Measurement, simulation, and modelling 声音在管弦乐队中的传播。测量、模拟和建模

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics

Pub Date : 2024-11-13 DOI: 10.1016/j.apacoust.2024.110389

Emanuele Porcinai, Stefan Weinzierl

One of the main challenges in predicting the room acoustic conditions on a stage is taking into account the presence of the ensemble and its effect on sound propagation. In a scenario where diffraction effects are dominant and the shape and arrangement of obstacles are not only highly complex but also time-varying, geometric acoustic methods do not yet provide sufficient accuracy for calculating room acoustic parameters or for auralisation. To address this limitation, anechoic measurements from a group of seated subjects were combined with Boundary Element Method simulations at lower frequencies to obtain broadband insertion loss values for a total of 104 paths within a typical orchestra setup. These transfer functions have been converted into a Diffraction-Induced Attenuation by Seated Persons FIR Database, which includes linear phase approximations of the direct sound as well as floor reflections, and reproduces the attenuation that occurs between players in an orchestra. Based on these filters, a parametric model was developed to predict insertion loss within different groups of seated people. This can be used in geometric acoustic simulations and auralisations to account for insertion loss within different groups of seated people, as they occur in many acoustically relevant everyday situations.

预测舞台房间声学条件的主要挑战之一是考虑到合奏的存在及其对声音传播的影响。在衍射效应占主导地位，障碍物的形状和排列不仅非常复杂，而且随时间变化的情况下，几何声学方法还不能提供足够准确的房间声学参数计算或听觉化。为了解决这一局限性，我们将一组坐着的受试者的消声测量结果与低频下的边界元法模拟相结合，获得了一个典型管弦乐队内总共 104 条路径的宽带插入损耗值。这些传递函数已被转换为坐姿人员衍射衰减 FIR 数据库，其中包括直达声和地板反射的线性相位近似值，并再现了管弦乐队中演奏者之间的衰减。在这些滤波器的基础上，开发了一个参数模型，用于预测不同坐位人群的插入损耗。该模型可用于几何声学模拟和听觉分析，以计算不同坐席人群内的插入损失，因为在许多与声学相关的日常情况中都会出现这种情况。

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引用次数: 0