Pub Date : 2024-11-15DOI: 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.
{"title":"Sound absorption mechanism and characteristic of a pressure-resistant sandwich structure supported by carbon fiber truss and embedded cavities in rubber core","authors":"Kangle Li , Liuwei Mao , Zihao Chen , Zhixin Huang , Zhiwei Zhou , Ying Li","doi":"10.1016/j.apacoust.2024.110386","DOIUrl":"10.1016/j.apacoust.2024.110386","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110386"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 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.
{"title":"Enhancing real-world far-field speech with supervised adversarial training","authors":"Tong Lei , Qinwen Hu , Zhongshu Hou , Jing Lu","doi":"10.1016/j.apacoust.2024.110407","DOIUrl":"10.1016/j.apacoust.2024.110407","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110407"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 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.
{"title":"Estimation of lung sound cycle span using spectro-temporal respiratory frequency evaluation","authors":"Irin Bandyopadhyaya, Premjeet Singh, Sudestna Nahak, Arnab Maity, Goutam Saha","doi":"10.1016/j.apacoust.2024.110390","DOIUrl":"10.1016/j.apacoust.2024.110390","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110390"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 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.
{"title":"Speech emotion recognition using multi resolution Hilbert transform based spectral and entropy features","authors":"Siba Prasad Mishra, Pankaj Warule, Suman Deb","doi":"10.1016/j.apacoust.2024.110403","DOIUrl":"10.1016/j.apacoust.2024.110403","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110403"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 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.
{"title":"A wideband damage source localization method using enhanced virtual time reversal mirror technique and modal analysis with sparse acoustic emission array","authors":"Kangwei Wang, Yang Qian, Jie Xie, Jun Wang, Weiguo Huang","doi":"10.1016/j.apacoust.2024.110405","DOIUrl":"10.1016/j.apacoust.2024.110405","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110405"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 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.
{"title":"The effect of time-varying characteristics of shallow-sea waveguides on low-frequency acoustic signal transmission","authors":"Lidong Huang , Zhuoyang Zou , Bin Wu , Wenrui Wang , Lingyun Ye","doi":"10.1016/j.apacoust.2024.110375","DOIUrl":"10.1016/j.apacoust.2024.110375","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110375"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 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.
{"title":"Application of a priori knowledge-enhanced fuzzy clustering to acoustic emission-based damage identification of composite laminates","authors":"Weijie Ma , Fan Dong , Yazhi Li , Biao Li , Chunping Zhou","doi":"10.1016/j.apacoust.2024.110404","DOIUrl":"10.1016/j.apacoust.2024.110404","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110404"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 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.
{"title":"Robust fiber-optic microphone with modified dual-wavelength demodulation algorithm for low-frequency sound detection","authors":"Xinyu Hu , Haibo Wang , Yan Yue , Lichao Zhang , Chenglong Zhang , Zhi-mei Qi","doi":"10.1016/j.apacoust.2024.110394","DOIUrl":"10.1016/j.apacoust.2024.110394","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110394"},"PeriodicalIF":3.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 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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Pub Date : 2024-11-13DOI: 10.1016/j.apacoust.2024.110393
Zhang Xin , Teng Xudong , Fan Yuantao
For detecting changes in air composition, the traditional method based on measuring the speed of sound lacks selectivity for different gaseous species and is easily influenced by environmental effects such as pressure, humidity, and temperature. Additionally, this method is difficult to be used for the quantitative analysis of air mixed with an unknown gas. In this paper, a data-driven model is developed for detecting changes in air composition from a qualitative perspective. By comparing the measured speed of sound with that theoretically calculated using the virial expansion for real air, the precise differences are used as data to construct a distance matrix, then the most typical speed difference is identified in order to calculate the z-score, from which the one-sided p-value (which is the probability of the z-score from a normal distribution) is calculated to detect a change in air composition at a given significance level. Experimental results show that the proposed data-driven model can accurately locate the time of change and determine the change intervals for air composition variations, and it has better accuracy and a lower value of RFT, almost equal to zero, compared with methods such as quartiles, standard deviation, interquartile range, and Bayesian detection and thus can be applied to domestic and industrial sensors for air monitoring, gas detection, and gas pollution alarms.
对于检测空气成分的变化，传统的测量声速方法缺乏对不同气体种类的选择性，并且容易受到压力、湿度和温度等环境影响。此外，这种方法很难用于对混有未知气体的空气进行定量分析。本文开发了一个数据驱动模型，用于从定性角度检测空气成分的变化。通过将测量到的声速与利用真实空气的病毒式膨胀理论计算出的声速进行比较，将精确的差异作为数据来构建距离矩阵，然后找出最典型的速度差异，以计算出 z 分数，并由此计算出单边 p 值（即 z 分数来自正态分布的概率），从而在给定的显著性水平下检测出空气成分的变化。实验结果表明，与四分法、标准偏差法、四分位数区间法和贝叶斯检测法等方法相比，所提出的数据驱动模型能准确定位空气成分变化的时间并确定变化区间，具有更好的准确性和更低的 RFT 值，几乎等于零，因此可应用于空气监测、气体检测和气体污染报警的家用和工业传感器。
{"title":"Detecting changes in air composition based on speed of sound","authors":"Zhang Xin , Teng Xudong , Fan Yuantao","doi":"10.1016/j.apacoust.2024.110393","DOIUrl":"10.1016/j.apacoust.2024.110393","url":null,"abstract":"<div><div>For detecting changes in air composition, the traditional method based on measuring the speed of sound lacks selectivity for different gaseous species and is easily influenced by environmental effects such as pressure, humidity, and temperature. Additionally, this method is difficult to be used for the quantitative analysis of air mixed with an unknown gas. In this paper, a data-driven model is developed for detecting changes in air composition from a qualitative perspective. By comparing the measured speed of sound with that theoretically calculated using the virial expansion for real air, the precise differences are used as data to construct a distance matrix, then the most typical speed difference is identified in order to calculate the <em>z</em>-score, from which the one-sided <em>p</em>-value (which is the probability of the <em>z</em>-score from a normal distribution) is calculated to detect a change in air composition at a given significance level. Experimental results show that the proposed data-driven model can accurately locate the time of change and determine the change intervals for air composition variations, and it has better accuracy and a lower value of <em>R<sub>FT</sub></em>, almost equal to zero, compared with methods such as quartiles, standard deviation, interquartile range, and Bayesian detection and thus can be applied to domestic and industrial sensors for air monitoring, gas detection, and gas pollution alarms.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"229 ","pages":"Article 110393"},"PeriodicalIF":3.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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