Applied Acoustics最新文献_第10页

Modulation recognition for underwater acoustic communication based on hybrid neural network and feature fusion 基于混合神经网络和特征融合的水下声学通信调制识别

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-31 DOI: 10.1016/j.apacoust.2024.110185

{"title":"Modulation recognition for underwater acoustic communication based on hybrid neural network and feature fusion","authors":"","doi":"10.1016/j.apacoust.2024.110185","DOIUrl":"10.1016/j.apacoust.2024.110185","url":null,"abstract":"<div><p>It is a huge challenge for underwater acoustic receivers to correctly identify modulation methods due to the complex underwater channel environment and severe noise interference. Combined with the lightweight network (SqueezeNet) and attention mechanism (SENet), a multi-attribute and multi-scale feature fusion model based on a hybrid neural network is proposed, which achieves efficient and accurate recognition for modulation modes. First, the wavelet time-frequency (WTF) spectrum, square power spectrum, and contour maps of cyclic spectrum are extracted as multi-attribute inputs for the network to reduce the impact of inherent defects in single attribute feature. Second, shallow and deep features based on the SqueezeNet model are obtained as multi-scale features, of which the key feature expression ability is enhanced by the SENet model to provide sufficient feature information for modulation recognition. The simulation experiments and sea trial data confirm that the suggested method demonstrates strong generalization capabilities and effectiveness when applied to underwater acoustic channels and environmental noise. In contrast to algorithms in existence, the method verifies superior recognition abilities.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932759","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}

引用次数: 0

Performance of micro-perforated muffler with flexible back cavity for water filled pipelines 带柔性后腔的微穿孔消声器在充水管道中的性能

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-31 DOI: 10.1016/j.apacoust.2024.110192

引用次数: 0

Optimal filter design using mountain gazelle optimizer driven by novel sparsity index and its application to fault diagnosis 利用新型稀疏性指数驱动的山羚优化器进行最佳滤波器设计及其在故障诊断中的应用

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-30 DOI: 10.1016/j.apacoust.2024.110200

引用次数: 0

A fault diagnosis method with AT-ICNN based on a hybrid attention mechanism and improved convolutional layers 基于混合注意力机制和改进卷积层的 AT-ICNN 故障诊断方法

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-29 DOI: 10.1016/j.apacoust.2024.110191

{"title":"A fault diagnosis method with AT-ICNN based on a hybrid attention mechanism and improved convolutional layers","authors":"","doi":"10.1016/j.apacoust.2024.110191","DOIUrl":"10.1016/j.apacoust.2024.110191","url":null,"abstract":"<div><p>Fault diagnosis is crucial for mechanical systems, with early diagnosis of bearings playing a key role in ensuring the overall safety and smooth operation of the mechanical system. However, in real industrial environments, traditional diagnostic methods limit the extraction of fault signals from rotating machinery. This study aims to improve the fault diagnosis method for critical mechanical components and proposes a novel deep learning model, the Attention Improved CNN (AT-ICNN) fault diagnosis method. The method combines Convolutional Neural Network (CNN) and attention mechanism to extract key fault feature information from signals, enhancing the model’s ability to highlight fault features and capture global information. This improves the accuracy of fault type identification. The AT-ICNN model enhances traditional CNN models by introducing Improved Convolutional (IMConv) and integrating a hybrid attention mechanism to effectively extract relevant fault information. Experimental results demonstrate superior diagnostic performance of AT-ICNN on the CWRU bearing dataset and laboratory bearing dataset, with accuracy rates of 98.12% and 98.72%, respectively. This represents about 9% improvement over baseline models and other advanced methods. In-depth analysis of experimental results validates the significant advantages of AT-ICNN in the field of fault diagnosis for critical mechanical components.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932841","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}

引用次数: 0

Psychoacoustic model for detecting the sensation of impulsivity in acoustic signals from refrigerators 从冰箱声学信号中检测冲动感觉的心理声学模型

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-29 DOI: 10.1016/j.apacoust.2024.110198

{"title":"Psychoacoustic model for detecting the sensation of impulsivity in acoustic signals from refrigerators","authors":"","doi":"10.1016/j.apacoust.2024.110198","DOIUrl":"10.1016/j.apacoust.2024.110198","url":null,"abstract":"<div><p>This study investigates the perception of impulsivity in audio signals specifically caused by thermal expansion (cracking noise) in domestic refrigerators. It employs a multifaceted approach encompassing an analysis of sensitivity to impulsive events, an assessment of the prominence of impulse signals, and a correlation analysis of these data. The investigation revealed different responses to various sound samples, highlighting subjective variations. Using linear regression, correlation analysis demonstrated a robust and positive relationship (Pearson correlation coefficient of 0.851) between perceived impulsivity and the Impulsivity Prediction Model (IPM). This alignment underscores the reliability of the developed IPM in capturing and predicting subjective perceptions of low-amplitude transient signals. Comparisons between groups of participants, conducted using both Analysis of Variance (ANOVA) and t-tests, explored potential disparities related to gender, age, and acoustic knowledge. The results indicated no statistically significant differences in the perception of impulsivity concerning gender, age groups, or acoustic knowledge. In conclusion, this study provides insights into the perceptual aspects of impulsivity in audio signals from home refrigerators, specifically addressing thermal expansion noises, and establishes the reliability of the Impulsivity Prediction Model (IPM) as a tool for objective assessment. The congruence between subjective judgments and objective metrics enhances the applicability of IPM in diverse fields, from acoustic engineering to psychoacoustic research.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932838","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}

引用次数: 0

Compressive spherical beamforming based on fast off-grid sparse Bayesian inference 基于快速离网稀疏贝叶斯推理的压缩球形波束成形

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-28 DOI: 10.1016/j.apacoust.2024.110190

{"title":"Compressive spherical beamforming based on fast off-grid sparse Bayesian inference","authors":"","doi":"10.1016/j.apacoust.2024.110190","DOIUrl":"10.1016/j.apacoust.2024.110190","url":null,"abstract":"<div><p>Compressive spherical beamforming (CSB) with spherical microphone arrays not only inherits the high spatial resolution and strong sidelobe suppression of compressive beamforming but also achieves panoramic acoustic source identification owing to the rotational symmetry of spherical microphone arrays, which is an interesting topic in the field of acoustic source identification. The recent off-grid sparse Bayesian inference-based CSB (OGSBI-CSB) can effectively overcome the basis mismatch of earlier on-grid CSB approaches and shows a higher resolution than the Newtonized orthogonal matching pursuit-based CSB (NOMP-CSB), however, it is severely time-consuming. Therefore, this paper proposes fast OGSBI-CSB (FOGSBI-CSB), which first solves an on-grid CSB model using sparse Bayesian inference to estimate the initial directions of arrival (DOAs), then performs DOA refinement by discretizing the local regions centered on the initial on-grid DOAs into finer grids and searching for candidates that can maximize the cost function, and finally quantifies source strengths utilizing the least squares method. Simulation and experimental results demonstrate that the proposed FOGSBI-CSB could provide a higher resolution than NOMP-CSB and a higher computational efficiency and resolution than OGSBI-CSB.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932839","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}

引用次数: 0

Experimental investigation on aerodynamic noise of a small-scale multi-blade centrifugal fan 小型多叶离心风机空气动力噪声实验研究

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-27 DOI: 10.1016/j.apacoust.2024.110184

Han Chen, Peiran Jiang, Hao Cheng, Pengfei Ma, Yu Liu

{"title":"Experimental investigation on aerodynamic noise of a small-scale multi-blade centrifugal fan","authors":"Han Chen, Peiran Jiang, Hao Cheng, Pengfei Ma, Yu Liu","doi":"10.1016/j.apacoust.2024.110184","DOIUrl":"https://doi.org/10.1016/j.apacoust.2024.110184","url":null,"abstract":"In this study, the aerodynamic noise of a small-scale centrifugal fan was experimentally investigated using acoustic testing techniques and time-resolved stereoscopic particle image velocimetry (SPIV). During the acoustic experiments, both far-field noise and near-field pressure fluctuations of the test fan were measured. The overall far-field noise towards the fan inlet side was found to be higher than that of the back side. The pressure fluctuations on the fan upper casing exceeded those on the side wall due to the uncontracted volute tongue, indicating pronounced flow-to-wall interactions. Moreover, based on a simultaneous measurement, the coherence between the near-field pressure fluctuations and far-field noise highlighted the significant contributions of impeller rotation to noise radiation. SPIV measurements uncovered the time-averaged and transient flow fields at the fan's inlet and outlet. The time-averaged results demonstrated the concentrated inlet flow and outlet flow separation, leading to high flow unsteadiness. Transient flow fields displayed an asymmetric jet-wake region characterized by both quasi-steady flow and rotational flow behaviours. The instantaneous flow results were analyzed using the dynamic mode decomposition (DMD) method, which clearly recognized the jet-wake patterns with frequencies corresponding to the rotational frequency. The observed consistency in frequency characteristics among noise, pressure fluctuations, and unsteady flow affirms that flow dynamics are crucial to the primary noise mechanisms.","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932758","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}

引用次数: 0

A Non-Metallic pipeline leak size recognition method based on CWT acoustic image transformation and CNN 基于 CWT 声学图像变换和 CNN 的非金属管道泄漏尺寸识别方法

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-26 DOI: 10.1016/j.apacoust.2024.110180

{"title":"A Non-Metallic pipeline leak size recognition method based on CWT acoustic image transformation and CNN","authors":"","doi":"10.1016/j.apacoust.2024.110180","DOIUrl":"10.1016/j.apacoust.2024.110180","url":null,"abstract":"<div><p>Accurately identifying the pipeline leak size is crucial for risk assessment and timely rescue. In this study, a Convolutional Neural Network (CNN) based on Continuous Wavelet Transform (CWT) acoustic image transformation is proposed to identify small-sized leak in non-metallic pipes. Firstly, one-dimensional acoustic signals are filtered using the Piecewise Aggregate Approximation (PAA) algorithm to reduce noise and storage resource consumption. Then, the filtered signals are transformed into two-dimensional images by CWT to enrich signal feature information, serving as the input for the CNN. Further, a leak size recognition model based on CWT-CNN is established. The effectiveness of this model is verified using experimental data from a non-metallic pipeline leak test. A comparative analysis is conducted on diverse acoustic image transformation methods, including CWT, Gramian Angular Summation Field (GASF), and Relative Position Matrix (RPM). The results demonstrate the superiority of the CWT-CNN model in pipeline leak size recognition. Finally, the impact of the signal length in an acoustic image on recognition accuracy is also examined. The results demonstrate that when the signal length in an acoustic image is 0.75 s, the accuracy obtained by CWT-CNN can reach 95 %.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949833","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}

引用次数: 0

Composite structure with porous material and parallel resonators for broadband sound absorption at low-to-mid frequencies 带多孔材料和平行谐振器的复合结构，用于中低频宽带吸音

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-25 DOI: 10.1016/j.apacoust.2024.110193

{"title":"Composite structure with porous material and parallel resonators for broadband sound absorption at low-to-mid frequencies","authors":"","doi":"10.1016/j.apacoust.2024.110193","DOIUrl":"10.1016/j.apacoust.2024.110193","url":null,"abstract":"<div><p>Herein, a broadband acoustic metamaterial composed of parallel Helmholtz resonators (PHR) with embedded channels and porous material (PM), is designed for low-to-mid-frequency noise absorption. A theoretical model of acoustic impedance is developed to illustrate the absorption characteristics of PHR–PM. The validity of the present model is confirmed by comparing the experimental results and numerical simulations. The PM may enhance the sound absorption performance of the PHR–PM by satisfying impedance matching conditions, which provides a new strategy for designing resonant systems with tunable sound-absorption characteristics. Both PM and PHR contribute to sound absorption, although their absorption capacities depend on the frequency ranges. The effects of structural and material parameters on sound absorption capacity are also analytically explored. Results indicate that sound absorption in the co-action and PM-dominated regions is mainly affected by material parameters, while that across the entire frequency range is considerably affected by structural parameters. Moreover, the average absorption coefficient of the 13HRs–PM may reach up to 0.6 at the frequency range of 100–1600 Hz, demonstrating its potential in achieving good broadband sound absorption performance and excellent absorption tenability. The proposed novel composite structure offers a new strategy for realizing high sound absorption at low-to-mid frequencies.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950923","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}

引用次数: 0

Signal latent subspace: A new representation for environmental sound classification 信号潜在子空间：环境声音分类的新表征

IF 3.4 2区物理与天体物理

Applied Acoustics Pub Date : 2024-07-25 DOI: 10.1016/j.apacoust.2024.110181

{"title":"Signal latent subspace: A new representation for environmental sound classification","authors":"","doi":"10.1016/j.apacoust.2024.110181","DOIUrl":"10.1016/j.apacoust.2024.110181","url":null,"abstract":"<div><p>In this study, we propose Signal Latent Subspace (SLS), a flexible method that classifies environmental sound events using the subspace representations of latent features obtained from various neural network-based models. Our main goal is to leverage the high expressiveness of neural networks while retaining the advantages of subspace representation, such as its robustness to noise and ability to work under small sample size (SSS) conditions. We also propose an ensemble strategy native to the subspace representation, to achieve increased performance and reduce the generalization error. We do this through product Grassmann manifold (PGM), resulting in SLS-PGM. Each subspace constructed from latent features of a network can be seen as a point on a factor Grassmann manifold (GM) of a neural network; through PGM, it is possible to unify factor manifolds into a singular representation, and perform classification through a similarity metric on the manifold. We further improve SLS and SLS-PGM in two ways: (1) by using generalized difference subspace (GDS) projection to address the lack of between-class discrimination of subspace representation and (2) by leveraging finetuning regimes to better adapt neural network models to the ESC task. We evaluate our proposed methods, factoring various neural networks, on ESC-10, ESC-50 and UrbanSound environmental sound datasets, and provide extensive ablation experiments and notes for practical use.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949834","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}

引用次数: 0