Acoustic Characteristics Research Articles

Investigation of anisotropic characteristics in ultra-precision grinding of sapphire crystal planes and strategies for suppression

Sapphire has been extensively utilized due to its excellent infrared transmittance. The demand for sapphire optical components has transitioned from flat geometries to more complex curved geometries. This transition necessitates the processing of sapphire from a single crystal plane to multiple crystal planes, presenting a significant challenge. To gain a comprehensive understanding of the anisotropy present in the machining of sapphire multiple crystal planes and to explore strategies for its suppression, various grinding wheels were employed for ultra-precision grinding of the sapphire A-plane, C-plane, M-plane, N-plane, and R-plane. This investigation encompassed multiple facets, including surface roughness, surface topography, residual stress, acoustic emission time-frequency characteristics, and infrared transmittance. A thorough analysis was conducted on the morphology of corrosion damage and the anisotropic mechanisms involved in sapphire ultra-precision grinding, as well as the resulting grinding quality following the implementation of suppression strategies. The findings indicate that the A-plane, C-plane, and M-plane of sapphire exhibit significant anisotropic characteristics when ground with the D25 resin bond grinding wheel, whereas the N-plane and R-plane do not display such characteristics. Furthermore, the application of the D3 ceramic bond grinding wheel effectively suppresses the anisotropy in the grinding of different sapphire crystal planes, achieving a high-quality surface finish with a ductile domain exceeding 90 %, and ensuring high uniformity in the ultra-precision grinding of various sapphire crystal planes.

Multifunctional acoustic and mechanical metamaterials prepared from continuous CFRP composites.

The imperative advance towards achieving "carbon neutrality" necessitates the development of porous structures possessing dual acoustic and mechanical properties in order to mitigate energy consumption. Nevertheless, enhancing various functionalities often leads to an increase in the structural weight, which limits the feasibility of using such structures in weight-sensitive applications. In accordance with the outlined specifications, a novel structural design incorporating carbon fiber reinforced polymer (CFRP) composites alongside mechanical and acoustic metamaterials has been introduced for the first time. This innovative construction exhibits a lightweight composition with excellent mechanical and acoustic characteristics. Experimental findings demonstrate that with meticulous planning and fabrication, CFRP composite structures can achieve a balance of lightweight construction, high strength, exceptional energy absorption, and remarkable resilience. By introducing membrane and reasonable cavity design, the structure can produce low broadband noise reduction performance by a local resonance effect and impedance matching mechanism of metamaterials. The structural sound insulation capability breaks traditional mass law, resulting in an exceptionally broadband sound insulation peak (bandwidth of nearly 1000 Hz). Furthermore, the sound absorption characteristic of the structure surpasses that of the melamine sponge at frequencies below 300 Hz, demonstrating superior low-frequency sound absorption properties. The proposed structure provides new approaches for the design of multifunctional lightweight superstructures.

Race Identification in American English.

This study examined the race identification of Southern American English speakers from two geographically distant regions in North Carolina. The purpose of this work is to explore how talkers' self-identified race, talker dialect region, and acoustic speech variables contribute to listener categorization of talker races. Two groups of listeners heard a series of /h/-vowel-/d/ (/hVd/) words produced by Black and White talkers from East and West North Carolina, respectively. Both Southern (North Carolina) and Midland (Indiana) listeners accurately categorized the race of all speakers with greater-than-chance accuracy; however, Western North Carolina Black talkers were categorized with the lowest accuracy, just above chance. The results suggest that similarities in the speech production patterns of West North Carolina Black and White talkers affect the racial categorization of Black, but not White talkers. The results are discussed with respect to the acoustic spectral features of the voices present in the sample population.

Self-supervised feature learning for acoustic data analysis

Acoustic surveys play a pivotal role in fisheries management. During the surveys, acoustic signals are sent into the water and the strength of the reflection, so-called backscatter, is recorded. The collected data are typically annotated manually, a process that is both labor-intensive and time-consuming, to support acoustic target classification (ATC). The primary objective of this study is to develop an annotation-free deep learning model that extracts acoustic features and improves the representation of acoustic data. For this purpose, we adopt a self-supervised method inspired by the Self DIstillation with NO Labels (DINO) model. Extracting useful acoustic features is an intricate task due to the inherent variability and complexity in biological targets, as well as environmental and technical factors influencing sound interactions. The proposed model is trained with three sampling methods: random sampling, which ignores class imbalance present in the acoustic survey data; class-balanced sampling, which ensures equal representation of known categories; and intensity-based sampling, which selects data to capture backscatter variations. The quality of extracted features is then evaluated and compared. We show that the extracted features lead to improvement, in comparison to using the untreated data, in the discriminative power of several machine learning methods (k-nearest neighbor (kNN), linear regression, multinomial logistic regression) for ATC. The improvement was measured through higher accuracy in kNN (77.55% vs. 71.93%), Macro AUC in logistic regression (0.92 vs. 0.80), and R2 in linear regression (0.69 vs. 0.45) when comparing extracted features to the untreated data. Our findings highlight the advantage of applying emerging self-supervised techniques in fisheries acoustics. This study thus contributes to the ongoing efforts to improve the efficiency of acoustic surveys in fisheries management.

Acoustic Transmission Loss of a Cylindrical Silencer Filled with Multilayer Poroelastic Materials Based on Mode-Matching Method

The efficacy of silencers in reducing piping noise is contingent upon the specific installation and operating environment. Among the various forms of silencers, the acoustic characteristics of dissipative silencers with sound-absorbing materials attached internally exist in an area that is difficult to explain by existing theories. This is dependent upon the specific type and placement of the attached sound-absorbing materials. This paper presents a methodology for calculating the acoustic transmission loss (TL) of a cylindrical silencer filled with a multilayer poroelastic material, employing the mode-matching method. To describe the numerical process of treating waves propagating within a poroelastic material and determine the modes in accordance with the boundary conditions necessary for analyzing the acoustic performance of the silencer, the Biot model and the Johnson–Champoux–Allard–Lafarge model were employed. The obtained modes were utilized to calculate the acoustic TL of silencers filled with single, double, and triple layers of poroelastic materials. In particular, the results obtained for the single layer were validated by comparing them with the results of a finite element analysis, and the results obtained for multiple layers with the same material were validated by comparing them with the equivalent single-layer results. Moreover, the results of the numerical calculations of the acoustic TLs of the silencer for three distinct types of poroelastic materials, including those with varying degrees of frame rigidity or softness, were compared, and the acoustic characteristics were analyzed in relation to the intrinsic properties of the materials and their arrangement. It is anticipated that the methodology presented in this paper will facilitate the design of silencers using poroelastic materials in accordance with the specific requirements of users or designers by allowing for a comprehensive consideration of the thickness of layers and the arrangement of materials.

The Impact of Clear and Loud Speech Cues on Acoustic and Perceptual Features of Speech Production in Adolescents With Down Syndrome

Purpose: There are few evidence-based speech interventions designed to alter speech production in a way that ultimately results in increased speech intelligibility in adolescents with Down syndrome (DS). The primary purpose of this study was to examine the impact of clear and loud speech cues on acoustic and perceptual features of speech production in adolescents with DS. Method: Eight adolescents diagnosed with DS repeated sentences of varying lengths in three conditions: habitual, big mouth (i.e., clear speech), and strong voice (i.e., loud speech). Four hundred eighty adult listeners (20 listeners per adolescent per condition) provided orthographic transcriptions of adolescent's speech, which were used to calculate intelligibility scores. Acoustic measures of speech rate, articulation rate, proportion of time spent pausing, vocal intensity, and fundamental frequency were calculated for each sentence. Results: The big mouth condition resulted in significantly increased intelligibility, slowed speech and articulation rates, increased pauses, increased vocal intensity, and increased fundamental frequency. The strong voice condition resulted in significantly increased vocal intensity and fundamental frequency, but no other changes. Speech rate was the only variable that explained any of the variance in intelligibility. Conclusions: Adolescents with DS respond differently to clear and loud speech cues. In particular, clear speech cues resulted in significant increases in intelligibility, but loud speech cues did not. Clear speech cues hold promise as an intervention strategy for adolescents with DS.

Decoding soundscape stimuli and their impact on ASMR studies

This paper focuses on extracting and understanding the acoustical features embedded in the soundscape used in ASMR (Autonomous Sensory Meridian Response) studies. To this aim, a dataset of the most common sound effects employed in ASMR studies is gathered, containing whispering stimuli but also sound effects such as tapping and scratching. Further, a comparative analytical survey is performed based on various acoustical features and two-dimensional representations in the form of mel spectrogram. A special interest is in whispering sounds uttered in different languages. That is why whispering sounds are compared in the language context, and the characteristics of speaking and whispering are investigated within languages. The results of the 2D analyses are shown in the form of similarity measures, such as Normalized Root Mean Squared Error (NRMSE), PSNR (peak signal-to-noise ratio), and SSIM (structural similarity index measure). The summary is produced, showing that the analytical aspect of the inherently experiential nature of ASMR is highly affected by the subjective, personal experience, so the evidence behind triggering certain brain waves cannot be unambiguous.

The Effect of Rhinoplasty on the Acoustic Characteristics of Resonance and Sound Production

The Effect of Rhinoplasty on the Acoustic Characteristics of Resonance and Sound Production

Research on the wave transmission characteristics of a flexible chamber inserted in the fluid-filled elastic pipeline

In the acoustic calculations of pneumatic pipeline systems, it is commonly assumed that the walls of the pipeline are rigid. It leads to the development of various acoustic theories based on rigid wall. However, in situations involving high-density fluids or thin-walled structures, the propagation of waves in the pipeline becomes complex due to the strong vibroacoustic coupling. In such cases, the wave transmission characteristics calculation methods based on the rigid wall assumption are no longer applicable. This paper employs the pipeline waveguide theory to estimate the wave transmission characteristics in the presence of complex wave behavior within the pipeline. Once the accuracy of the proposed method has been verified, the wave responses of a flexible chamber inserted in the fluid-filled elastic pipeline are analyzed. The analysis shows that the lowest-order fluid-type waves and the lowest-order longitudinal extension waves are found to propagate in the pipeline. The incident lowest-order fluid-type wave and its transmitted wave of the same mode can be successfully separated from other waves. The elasticity of the pipe wall can significantly affect the acoustic characteristics of an expansion chamber. Nevertheless, significant disparities in the wave amplitudes and limited sampling pose challenges to accurate calculations of the transmission loss.

Lightweight, high-strength, thermal- and sound-insulating reed scraps/portland cement composite using extruded resin particles

The development of a lightweight composite made from reed scraps, Portland cement, and polystyrene particles can simultaneously address issues related to reed waste, environmental pollution, and carbon emissions produced by the construction industry. However, standard polystyrene particles have low bonding strength with Portland cement and reed scraps, giving the resulting composites inferior mechanical properties and thermal and acoustic insulation characteristics. To overcome these limitations, nano-silicon expanded resin particles (NSERP), composed of polyvinyl alcohol, polystyrene, nano-silicon, and various modifiers, were introduced to replace traditional polystyrene particles in the composite. These novel particles enhanced the interfacial bonding strength and improved the mechanical properties, as well as the thermal and acoustic insulation capabilities of the composites. This study investigated the effects of the water-cement ratio, reed scraps-to-cement ratio, and NSERP dosage on the composite's properties through single-factor experiments. The optimized composite had a density of 0.66 g/cm³ and a compressive strength of 2.5 MPa, and its sound absorption coefficient in the middle and high-frequency bands was 0.7, which was approximately 4.3 times higher than that of conventional silicate composites. Its thermal insulation performance surpassed that of standard composites. This lightweight, high-strength, thermally insulating, environmentally friendly, and energy-efficient multifunctional composite has potential applications in building energy conservation, traffic noise reduction, and new energy battery insulation.

A Comparative Analysis of Bhumija-Style Gondeshwar Temple’s Acoustic Characteristics with Hemadpanti-Style Temples

ABSTRACT The study of the Hindu temple’s acoustics, acoustic characteristics, sound quality for clarity of music and speech intelligibility, and other applicable acoustic parameters must be addressed more in the literature. This study has conducted field measurements in Bhumija-style Gondeshwar temples in Nashik district, India. The temple has a distinctive tiered domed concave ceiling design in the pavilion and sanctum space and unique acoustic properties shaped by its architectural features. The research aims to report and investigate the acoustic characteristics through the well-known Impulse Response (IR) method to investigate empty temple conditions. A 3D model was imported and calibrated in ODEON 12.10 to compare scenarios when the worshippers were 100% present in the temple for various musical and chanting activities. Objective room-acoustic parameters such as Early Decay Time (EDT), Reverberation Time (T30), Clarity of Music (C80), Definition (D50), and Speech Transmission Index (STI) were measured and analyzed. The field measurements were also compared with previously studied Hemadpanti-style Hindu temples, such as the Markanda and Mrikunda temples. The results indicated that the tiered ceiling in Hemadpanti temples indicates a balance and moderate T30 values for all frequency ranges, as compared to the tiered concave domed ceiling in Bhumija-style Gondeshwar temple with higher T30 values observed at the lower frequency.

Relationships Between Cranial Phantom Physical Characteristics and Transcranial Acoustic Wave Signal Features

Relationships Between Cranial Phantom Physical Characteristics and Transcranial Acoustic Wave Signal Features

Fixed frequency range empirical wavelet transform based acoustic and entropy features for speech emotion recognition

The primary goal of automated speech emotion recognition (SER) is to accurately and effectively identify each specific emotion conveyed in a speech signal utilizing machines such as computers and mobile devices. The widespread recognition of the popularity of SER among academics for three decades is mainly attributed to its broad application in practical scenarios. The utilization of SER has proven to be beneficial in various fields, such as medical intervention, bolstering safety strategies, conducting vigil functions, enhancing online search engines, enhancing road safety, managing customer relationships, strengthening the connection between machines and humans, and numerous other domains. Many researchers have used diverse methodologies, such as the integration of different attributes, the use of different feature selection techniques, and designed a hybrid or complex model using more than one classifier, to augment the effectiveness of emotion classification. In our study, we used a novel technique called the fixed frequency range empirical wavelet transform (FFREWT) filter bank decomposition method to extract the features, and then used those features to accurately identify each and every emotion in the speech signal. The FFREWT filter bank method segments the speech signal frame (SSF) into many sub-signals or modes. We used each FFREWT-based decomposed mode to get features like the mel frequency cepstral coefficient (MFCC), approximate entropy (ApEn), permutation entropy (PrEn), and increment entropy (IrEn). We then used the different combinations of the proposed FFREWT-based feature sets and the deep neural network (DNN) classifier to classify the speech emotion. Our proposed method helps to achieve an emotion classification accuracy of 89.35%, 84.69%, and 100% using the combinations of the proposed FFREWT-based feature (MFCC + ApEn + PrEn + IrEn) for the EMO-DB, EMOVO, and TESS datasets, respectively. Our experimental results were compared with the other methods, and we found that the proposed FFREWT-based feature combinations with a DNN classifier performed better than the state-of-the-art methods in SER.

Can acoustic measurements predict gender perception in the voice?

To determine if there is an association between vocal gender presentation and the gender and context of the listener. Quantitative and transversal study. 47 speakers of Brazilian Portuguese of different genders were recorded. Recordings included sustained vowel emission, connected speech, and the expressive recital of a poem. Subsequently, four scripts were used in Praat to extract 16 acoustic measurements related to prosody. Voices underwent Auditory-Perceptual Assessment (APA) of the gender presentation by 236 people [65 speech and language pathologist (SLP) with experience in the area of the voice (SLP), 101 cisgender people (CG), and 70 transgender and non-binary people (TNB)]. Gender presentation was evaluated by visual analogue scale. Agreement analyses were executed among quantitative variables and multiple linear regression models were generated to predict APA, taking the judge context/gender and speaker gender into consideration. Acoustic analysis revealed that cis and transgender women had higher median fundamental frequency (fo) values than other genders. Cisgender women exhibited greater breathiness, while cisgender men showed more vocal quality deviations. In terms of APA, significant differences were observed among judge groups: SLP judged vowel samples differently from other groups, and TNB judged speech samples differently (both p<0.001). The predictive measures for the APA varied based on the sample type, speaker gender, and judge group. For vowel samples, only SLP judges had predictive measures (fo and ABI Jitter) for cisgender speakers. In number counting samples, predictive measures for cisgender speakers included fomed and HNR for CG judges, and fomed for both SLP and TNB judges. For transgender and non-binary speakers, predictive measures were fomed for CG and SLP judges, and fomed, CPPs, and ABI for TNB judges. In the poem recital task, predictive measures for cisgender speakers were fomed and HNR for both SLP and CG judges, with additional measures of cvint and sr for CG judges, and fomed, HNR, cvint, and fopeakwidth for TNB judges. For transgender and non-binary speakers, the predictive measures included a wider range of acoustic features such as fomed, fosd, sr, fomin, emph, HNR, Shimmer, and fo peakwidth for SLP judges, and fomed, fosd, sr, fomax, emph, HNR, and Shimmer for CG judges, while TNB judges considered fomed, sr, emph, fosd, Shimmer, HNR, Jitter, and fomax. There is an association between the perception of gender presentation in the voice and the gender or context of the listener and the speaker. Transgender and non-binary judges diverged to a higher degree from cisgender and SLP judges. Compared to the evaluation of cisgender speakers, all judge groups used a greater number of acoustic measurements when analyzing the speech of transgender and non-binary individuals in the poem recital samples.

Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features.

In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms' deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior.

Study on mechanical properties and acoustic emission characteristics of composite rock mass with different thicknesses of weak interlayer

To study the influence of the thickness of a weak interlayer on the deformation and failure characteristics of composite rock mass. Based on the measured data of sand-mudstone interbedded floor rock mass in the Yima mining area, Henan Province, China. Using quartz sand, gypsum, and cement as similar materials make composite rock-like specimens with weak interlayers. The mechanical properties, deformation evolution process, failure characteristics, and acoustic emission laws of rock samples with different interlayer thicknesses under uniaxial compression were studied using the digital image correlation method and acoustic emission monitoring technology. The results show that: (1) With an increase in the thickness of the weak interlayer, there is a corresponding decrease in the compressive strength of the specimen. When the thickness of the weak interlayer exceeds 5 cm, the compressive strength of the composite rock specimen is even less than that of the single weak rock-like. (2) Under uniaxial compression conditions, the strain concentration zone first appears in the weak interlayer part of the composite rock-like specimen. As the pressure continues to increase, the specimen is the first to fail at the position of the maximum strain concentration zone. (3) The thickness of the weak interlayer is an important factor affecting the failure mode of composite rock-like specimens. With the increase of the thickness of the weak interlayer, the composite rock-like specimens change from overall failure to local failure. (4) With the increase of the thickness of the weak interlayer in the middle of the composite rock specimen, the maximum value of the energy released by the single acoustic emission event and the maximum value of the cumulative acoustic emission energy decrease during the compression failure process. The research results can provide a reference for the manufacture of composite rock mass with a weak interlayer and the deformation and failure analysis of composite rock mass.

Spalling behavior in ultra-high performance concrete: multi-technique insights and multi-scale fiber-rubber mitigation strategies

Explosive spalling of ultra-high performance concrete (UHPC) at elevated temperatures is one of the main problems limiting its application. While the intrinsic mechanism governing the spalling is determined by the UHPC matrix. Therefore, in this paper, multi-technique characterization approaches, i.e., acoustic signal characteristics, fractal characteristics of spalling fragments, and thermal cracks are employed to quantitatively characterize the spalling of the UHPC matrix. The effects of moisture content, heating rate, geometric characteristics of specimens, mineral admixtures, crumb rubber content, and fibers on concrete spalling are investigated to reveal the explosive spalling mechanism, reduce the risk of explosive spalling, and develop a spalling-resistant material. The waveform, cumulative energy, and spectrum of the spalling acoustic signal are used to assess the intensity of concrete spalling. The results show that the UHPC matrix with high moisture content, high heating rate, and large specimen size undergoes violent spalling events characterized by larger amplitude and smaller frequencies of acoustic signals. Incorporating crumb rubber increases the spalling events in the frequency range of 8000–10000 Hz, which plays a positive role in alleviating spalling. Meanwhile, the fractal characteristics of spalling fragments and thermal cracks can also quantitatively reflect the intensity of concrete spalling (i.e., the larger the fractal dimension, the more severe spalling). By minimizing the moisture content and heating rate, as well as increasing rubber content, the spalling fragments can be larger and the fractal dimension can be reduced. It is also found that saturated specimens spall with significant cumulative energy. While the specimens with a large size and low moisture content spall with lower cumulative energy and an increase in the proportion of spalling fragments in the small particle size range. A higher heating rate and larger specimens can induce a higher thermal stress, which can be more likely to spall explosively. While a lower moisture content can result in explosive spalling at a higher heating rate. Hence, it is believed that thermal stress can be the dominant factor affecting explosive spalling and the moisture content can be an accelerator. The addition of multi-scale fibers completely prevents explosive spalling by releasing vapor pressure from the channels created by fiber melting and improving the tensile strength of concrete, which also effectively restricts the thermal cracking of UHPC due to their hybrid bridging effects.

The voice of depression: speech features as biomarkers for major depressive disorder

BackgroundPsychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures.MethodsForty-four MDD patients from the Psychiatry Department, University Hospital Aachen, Germany and fifty-two HCs were recruited. Participants described positive and negative life events, which were recorded for analysis. The Beck Depression Inventory (BDI-II) and the Hamilton Rating Scale for Depression gauged depression severity. Transcribed audio recordings underwent feature extraction, including acoustics, speech rate, and content. Machine learning models including speech features and neuropsychological assessments, were used to differentiate between the MDD patients and HCs.ResultsAcoustic variables such as pitch and loudness differed significantly between the MDD patients and HCs (effect sizes \U0001d73c2 between 0.183 and 0.3, p < 0.001). Furthermore, variables pertaining to temporality, lexical richness, and speech sentiment displayed moderate to high effect sizes (\U0001d73c2 between 0.062 and 0.143, p < 0.02). A support vector machine (SVM) model based on 10 acoustic features showed a high performance (AUC = 0.93) in differentiating between HCs and patients with MDD, comparable to an SVM based on the BDI-II (AUC = 0.99, p = 0.01).ConclusionsThis study identified robust speech features associated with MDD. A machine learning model based on speech features yielded similar results to an established pen-and-paper depression assessment. In the future, these findings may shape voice-based biomarkers, enhancing clinical diagnosis and MDD monitoring.

Meta-analysis of the acoustic adaptation hypothesis reveals no support for the effect of vegetation structure on acoustic signalling across terrestrial vertebrates.

Acoustic communication plays a prominent role in various ecological and evolutionary processes involving social interactions. The properties of acoustic signals are thought to be influenced not only by the interaction between signaller and receiver but also by the acoustic characteristics of the environment through which the signal is transmitted. This conjecture forms the core of the so-called "acoustic adaptation hypothesis" (AAH), which posits that vegetation structure affects frequency and temporal parameters of acoustic signals emitted by a signaller as a function of their acoustic degradation properties. Specifically, animals in densely vegetated "closed habitats" are expected to produce longer acoustic signals with lower repetition rates and lower frequencies (minimum, mean, maximum, and peak) compared to those inhabiting less-vegetated "open habitats". To date, this hypothesis has received mixed results, with the level of support depending on the taxonomic group and the methodology used. We conducted a systematic literature search of empirical studies testing for an effect of vegetation structure on acoustic signalling and assessed the generality of the AAH using a meta-analytic approach based on 371 effect sizes from 75 studies and 57 taxa encompassing birds, mammals and amphibians. Overall, our results do not provide consistent support for the AAH, neither in within-species comparisons (suggesting no overall phenotypically plastic response of acoustic signalling to vegetation structure) nor in among-species comparisons (suggesting no overall evolutionary response). However, when considering birds only, we found weak support for the AAH in within-species comparisons, which was mainly driven by studies that measured frequency bandwidth, suggesting that this variable may exhibit a phenotypically plastic response to vegetation structure. For among-species comparisons in birds, we also found support for the AAH, but this effect was not significant after excluding comparative studies that did not account for phylogenetic non-independence. Collectively, our synthesis does not support a universal role of vegetation structure in the evolution of acoustic communication. We highlight the need for more empirical work on currently under-studied taxa such as amphibians, mammals, and insects. Furthermore, we propose a framework for future research on the AAH. We specifically advocate for a more detailed and quantitative characterisation of habitats to identify frequencies with the highest detection probability and to determine if frequencies with greater detection distances are preferentially used. Finally, we stress that empirical tests of the AAH should focus on signals that are selected for increased transmission distance.

A unified Jacobi-Ritz-spectral BEM for vibro-acoustic behavior of spherical shell

In order to solve the vibration and acoustic characteristics of spherical shell in light fluid environment, based on Jacobi-Ritz-spectral BEM, a unified analysis formula for acoustic vibration of spherical shell under arbitrary boundary conditions is established. Based on the First-order shear deformation theory (FSDT) and domain decomposition method (DDM), the theoretical model of spherical shell structure is established. The improved Jacobi polynomial is innovatively used to construct the displacement tolerance function of the spherical shell. Based on the spectral Kirchhoff-Helmholtz integral formula, the theoretical model of the acoustic fluid outside the spherical shell is established. The special form of Jacobi polynomial Chebyshev polynomial is used to describe the excitation sound pressure on the spherical shell segment, so as to ensure that the generalized coordinates of the structure can be perfectly matched with the acoustic boundary element nodes. In addition, the integration along the meridian of the shell is used to control the movement of the fluid, which simplifies the surface integration. The CHIEF method is used to solve the problem of non-unique solution of acoustic variables of rotary structure. Compared with the published literature, numerical simulation results and experimental results, the proposed method has higher calculation accuracy. In addition, based on this method, the influence of boundary conditions, geometric dimensions and other factors on the acoustic and vibration characteristics of spherical shells is discussed, which accumulates data for analyzing the acoustic and vibration behavior of spherical shells.