Acoustic Applications Research Articles

Investigating cepstral methods for blind deconvolution

Cepstral methods are homomorphic signal processing techniques in which signals are transformed into the cepstral domain, typically to be averaged or filtered. In the cepstral domain, the convolution of the source signal and impulse response changes to a sum of the two parts by taking a logarithm in the frequency domain, followed by an inverse Fourier transform. Cepstral methods have been utilized in speech processing to separate vocal tract information from speech excitation, in marine mammal bioacoustics to classify vocalizations, and in seismic surveys to estimate source wavelets from airgun arrays, but do not appear to have been considered for blind deconvolution in other underwater acoustic applications. By assuming either the source signal or impulse response is stationary in time and/or space, averaging cepstral domain windows from a receiver array can suppress the non-stationary term. Here, we investigate the capabilities of cepstral averaging techniques for the purposes of blind deconvolution. We will exploit the spatial dependence of the impulse response across a receiver array to suppress this non-stationary term, while constructively combining the source signal term. We will consider the effects of different signal types, environmental characteristics, signal-to-noise ratios, and array design on its success. [Work supported by the ONR.]

Limitations of the asymptotic equations for exterior sound waves

The determination of exterior sound field due to incident plane waves on a cylinder may be of importance in many areas of acoustics. Applications in acoustics include detecting, locating and classifying objects that generate a scattered sound field excited by the incident sound waves. The exterior sound field in the acoustic domain due to incident plane waves on a cylinder can be determined analytically and numerically. However, it has been observed that for convergence the asymptotic expressions of the analytical models used in the literature required infinite number of terms in the series calculations or are seldom restricted either by low or high frequency limit or the requirement of near-field and far-field approximations. This paper investigates the reasons that caused the nonconformity of the asymptotic analytical expressions. The investigation thus carried out based on the numerically determined exterior sound field using the boundary element method (BEM) and compared with the results obtained using the governing expressions. The comparisons showed good agreement when the governing expressions are not restricted, but nonconformity occurs when they are restricted.

Sustainable thermo-acoustical insulation material from cardboard waste and natural fibers: Elaboration and performance evaluation

The main objective of this paper is to elaborate and characterize new ecological composites based on cardboard waste and abandoned natural fibers, in the Drâa-Tafilalet region (South-East, Morocco), for the manufacture of local thermo-acoustical insulation panels. For this study, 25 samples were prepared by mixing 60% of cardboard waste and 40% of vegetable fibers (Reed tree, esparto fiber, fig tree, and Olive tree). The morphological analysis of the different fibers was carried out by scanning electron microscopy (SEM), while, the physical, thermal, and acoustical properties of samples were measured experimentally using standard methods. The experimental results showed that all new composites have better thermal and acoustical performances comparable to those of synthetic insulation materials. The density, thermal conductivity, thermal diffusivity, and sound absorption coefficient of those composites were in the range of 278.6–343.8 kg/m3; 0.072–0.10 W/m·K; 1254.5–1807.5 J/kg·K; 0.4–0.8, respectively. Consequently, the by-products recovered in this study are good candidates for the development of local insulation materials with useful properties for thermal and acoustical insulation applications in buildings, low environmental impact, low cost, and competition with commercialized synthetic insulation materials.

Tailoring the surface and interface states in a three-dimensional fragile topological insulator with kagome lattice

Topological insulators, which are insulating in the bulk and conducting on the surface, have attracted great interest in recent years. The recently discovered topological surface and hinge states in three dimensions have provided promising methods for wave manipulation in classical systems. However, the combination of surface states and interface states has yet to be achieved in phononic crystals due to the missing topological surface states. Here, we experimentally demonstrate a fragile topological insulator with a bilayer kagome lattice. We observe the surface states in three different orientations and interface states formed by inversion of layer pseudospin sectors or bands. The combination of surface and interface states is investigated in $\mathsf{X}$-shaped waveguides. Our work may provide a platform for exploring unique acoustic applications based on surface or interface states.

Design of microperforated nanofibrous membrane coated nonwoven structure for acoustic applications

In this paper, a promising acoustic structure for noise reduction was prepared, in which microperforated nanofibrous resonant membrane together with nonwovens were used. The role of microperforated nanofibrous film, the effect of perforation parameters, cavity and the assembly sequence of the composite fibrous structure on sound absorption performance has been studied. This structure effectively combined the porous sound absorbing, micro-perforated absorbing and membrane resonance mechanisms, which can improve the sound absorbing performance without weight and thickness penalty offering a competitive advantage in noise reduction. In addition, the composite materials exhibited favorable performance in a wide-frequency regime under the condition of appropriate assembly sequence and perforation parameters.

Deep learning-assisted multifunctional wavefront modulation with Willis coupling

Diverse wavefront modulations with multifunctional acoustic devices have been of great interest to physics and engineering communities. However, traditional design methods of multifunctional acoustic devices rely on a deterministic physical model and redundant iterative optimization, resulting in inflexibility and consuming of time. In this work, we present and experimentally implement a deep learning-assisted tunable acoustic metagrating for multifunctional wavefront modulation with 95.2% accuracy and a 105 order of magnitude decrease in computational time compared to a classical optimization method. The presented tunable structure formed by a periodic array of 3C-shaped unit cells excites controllable Willis coupling, exhibiting corresponding asymmetrical scattering patterns. With the support of a deep learning strategy, the optimal configuration between structure parameters and Willis coupling magnitude could be efficiently confirmed, realizing various extraordinary wavefront modulations, including abnormal reflection, perfect beam splitting, and multi-channel energy distribution in arbitrary ratios. The polarizability tensor retrieval method is used to characterize the Willis coupling of different modulation structures, demonstrating the refined abstraction of the deep learning strategy on Willis coupling. Meanwhile, the numerical and experimental results are in good agreement with the desired wavefront modulation, verifying the effectiveness of the proposed method. Our work develops deep learning-assisted multifunctional wavefront modulation with the advantages of high accuracy, efficiency, flexibility, and refined abstraction of a physical mechanism, paving the way for a combination of deep learning and pragmatic multifunctional acoustic applications.

Design and Experimental Verification of a Broadband Multiphase Pentamode Material

Pentamode material (PM) possesses great potential in underwater acoustic metamaterial device applications due to its broadband and solid-state merits. Previously proposed PM devices are all designed with single materials, where the additional weights for mass adjustment are connected directly to the struts for modulus adjustment. The coupling effects between them severely restrict available fabrication techniques and the realizable range of physical properties of PM devices. A multiphase PM configuration, the additional weights of which are bonded to the struts with interconnecting materials, is proposed, and a waterlike multiphase PM device based on the proposed configuration is developed to assess the validity of the multiphase PM configuration. It is revealed from both simulation and experimental results that the acoustic properties of the proposed multiphase PM device mimics water within the simulation frequency range of 3--24 kHz. The fabrication cost of the proposed multiphase PM device is reduced by 80%, and the duration is only 1/15 when compared to that of a single-phase PM device. Additionally, the multiphase PM has great advantages over single-phase PM for aspects such as withstanding higher hydrostatic pressure due to much more uniform stress distribution and broadening the realizable range of physical properties of PM devices significantly.

The application of acoustics to heat engines and refrigerators.

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.

Broadband and high-numerical-aperture sharp focusing for waterborne sound with metagrating-based lens

Metalens with broadband and high-efficiency focusing functionality is desired in various underwater acoustic applications such as sonar and oceanography. Here we design and demonstrate a metagrating-based lens consisting of spatially sparse and wavelength-scale meta-atoms with optimized structures. With the help of grating diffraction analysis and intelligent optimization algorithm, the reflective metalens enables broadband and high-numerical-aperture focusing for waterborne sound over a 40 kHz-bandwidth for working frequency at 200 kHz. Full-wave numerical simulations unambiguously verify a sharp and high-efficiency focusing of sound wave intensity, with the full width at half maximum at the focal spot being smaller than 0.5λ and thus beating the Rayleigh–Abbe diffraction limit. Our work not only provides an intelligent design paradigm of high-performance metalens, but also presents a potential solution for the development of planar acoustic devices for high-resolution applications.

UHMWPE textiles and composites

Ultra-high molecular weight polyethylene (UHMWPE) has the potential to make a significant contribution to the efforts currently being made to help to protect the environment by reducing carbon emissions through the substitution of heavy conventional materials with lightweight polymeric materials. Used on its own, UHMWPE also offers complete recyclability with thermoplastic matrices. UHMWPE fibre-based composites (both thermoplastic and thermoset) offer a wide range of applications in various fields such as military protective suits, automotive, aerospace, electronics hardware, tribological application, and biomaterial implants, and this issue of Textile Progress explores the behaviour of UHMWPE with different matrix systems for various purposes. UHMWPE is widely used in the development of ballistic protective armours. Apart from applications where impact resistance is a key requirement, UHMWPE-based composites are currently being employed in the fields such as biomedical implants, anti-friction systems, dielectric and acoustic applications, and other structural fields; the UHMWPE should be extractable from the thermoplastic types and be able to be recycled. The various manufacturing techniques employed in the preparation of UHMWPE and its composites are discussed as are improvements aimed at eradicating existing processing issues associated with UHMWPE.

Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models

Wavelength division multiplexing (WDM), the technology that utilizes different wavelengths of carrier signal as independent information channels and helps to realize enhanced information density and functional diversity, is desired in various acoustic applications for the growing demand of higher transmission efficiency and lower cost. In this paper, we marry the rising concept of acoustic metagrating with the technology of WDM and demonstrate that interesting and distinct wavelength-dependent functionalities with unitary efficiency in prespecified frequency ranges can be realized by a simple grating structure. To this end, we integrate the particle swarm optimization with the deep learning technique to resolve the uncertainty issue and multi-objective problem and develop both deterministic and probabilistic neural networks for the inverse design of a metagrating-based WDM device. As a result, the computational cost is greatly reduced, and the probabilistic model reveals the sensitivity and flexibility of the parameters of the metagrating with respect to the desired functionality. In this paper, we not only provide an intelligent inverse design paradigm of high-performance WDM devices for multiple manipulation purposes but also present a feasible solution for the development of integrated acoustic devices for wavelength-dependent applications.

Deep learning approach for designing acoustic absorbing metasurfaces with high degrees of freedom

The advent of the acoustic metasurfaces offered an unprecedented expansion of our ability to manipulate and structure sound waves for many exotic functionalities. However, the metasurface designs require deep expertise in acoustics and highly intensive iterative computations using the Finite Element Method. In this work, we use a two-dimensional convolutional neural network to model complex metasurface absorber structures under oblique wave incidences. The proposed network architecture is capable of computing the absorption spectra with a large number of design degrees of freedom. Further, we implement a conditional generative adversarial network to solve the inverse design problem. When fed with an input set of predefined absorption spectra, the constructed generative network produces candidate designs of metasurface absorbers that match on-demand spectra with high fidelity. We demonstrate the capability of the implemented network architecture by designing a metasurface absorber operating at 82 Hz for oblique wave incidences with a thickness of λ/64. To implement the deep learning methods for acoustic designs, the main challenge is to generate large and high-quality training dataset using numerical simulations. To mitigate this issue, we implement data augmentation. The presented approaches open new avenues to automate the design process of metasurfaces and enable a much more generalized and broader scope of optimal designs that go beyond acoustics applications.

Chemical cyanide treatment of polycrystalline p-Cu2O/n-ZnO solar cell

Cuprous oxide (Cu2O) and zinc oxide (ZnO) hold a very crucial position in the field of materials research because of their immense potential in many acoustic, electronic and optical applications. Cu2O is a direct-gap semiconducting material with forbidden energy gap around 2.0 eV. It has been considered as one of the most promising materials for photovoltaic applications, especially for use at the top in a cascade cell structure. The enchantment of Cu2O and ZnO as photovoltaic materials is due to fact that their constituent materials are nontoxic, abundantly available and of relatively low cost. In this work, Cu2O thin films in polycrystalline form were prepared in radio-frequency (rf) reactive magnetron sputtering route on glass substrates at different substrate temperatures. After the samples were prepared, these were taken through a crown-ether cyanide (CN) treatment. Presence of oxygen in excess compared to stoichiometry is the major active impurity in Cu2O and consequently holes are the majority charge carriers in it. With the aid of crown-ether cyanide treatment of the prepared samples, hole-mobility was found to increase and resistivity was found to decrease considerably. Finally, polycrystalline p-Cu2O/n-ZnO heterojunctions were fabricated for use in solar cells. Results of Hall measurement strongly support the observations made from DLTS studies and photocurrent measurements. Thus, our present study confirms the improvement of device performance of the p-Cu2O /n-ZnO heterojunctions on chemical cyanide treatment.

Sounds in the desert: New evidence of ambos in Shivta churches

ABSTRACT A hexagonal marble object from the Rockefeller Museum, Jerusalem, found during excavations of H. D. Colt in the 1930s, has been identified as a canopy of an ambo. It probably belonged to one of the churches in Byzantine Shivta and is published here for the first time. Dated most likely to the 6th–early 7th century, it constitutes rare evidence of this liturgical furnishing in the Negev, raising questions concerning its unique iconography and acoustic applications.

An Underwater Acoustic Network Positioning Method Based on Spatial-Temporal Self-Calibration.

The emergence of underwater acoustic networks has greatly improved the potential capabilities of marine environment detection. In underwater acoustic network applications, node location is a basic and important task, and node location information is the guarantee for the completion of various underwater tasks. Most of the current underwater positioning models do not consider the influence of the uneven underwater medium or the uncertainty of the position of the network beacon modem, which will reduce the accuracy of the positioning results. This paper proposes an underwater acoustic network positioning method based on spatial-temporal self-calibration. This method can automatically calibrate the space position of the beacon modem using only the GPS position and depth sensor information obtained in real-time. Under the asynchronous system, the influence of the inhomogeneity of the underwater medium is analyzed, and the unscented Kalman algorithm is used to estimate the position of underwater mobile nodes. Finally, the effectiveness of this method is verified by simulation and sea trials.

(Invited) Graphene Oxide-Based Membranes: Viscoelastic Properties and Application to Broadband Microspeakers

Graphene and graphene derivatives have attracted attention as materials for acoustic transduction [1-3]. Graphene oxide (GO) membranes and borate cross-linked graphene oxide (X-GO) membranes combine high stiffness, low mass density, and high loss coefficient. We show [4] that these mechanical properties are ideal for efficient, broadband, electro-acoustic transduction, where the acoustic diaphragm should be light, stiff, and internally damped. We present a quantitative comparison of the figure of merit, (E/ρ 3)1/2, where E is Young's modulus and ρ is the mass density, and the material performance index, tan δ (E/ρ 3)1/2, where tan δ is the loss coefficient. GO and X-GO exhibit high figures of merit, comparable to that of wood and exceeding that of aluminum and thermoplastics. The material performance indices of GO and X-GO exceed that of wood, thermoplastics, and aluminum. The latter are common diaphragm materials in acoustic transducers.For acoustic applications, it is important to understand the frequency dependent viscoelastic response. The complex modulus G = G' + iG'' of GO and X-GO membranes was measured by dynamic mechanical analysis and analyzed by the application of the time-temperature superposition (TTS) principle of polymer rheology. We find that X-GO shows a more than 30% increase in storage modulus G’ over the frequency range of 1 to 10 kHz, exceeding 55 GPa at 10 kHz. There is a reduction in loss coefficient tan δ = G''/G' for X-GO of ≈30% over the same frequency range, reaching tan δ = 0.04 at 10 kHz, which is approximately one order of magnitude larger than that of aluminum.A quantitative comparison of microspeaker performance was conducted at a fixed diaphragm mass m = 15-20 g and 14 mm x 8 mm size, including GO, X-GO, oak wood, polyethylene terephthalate (PET), aluminum, and titanium. Scanning laser measurements confirm pistonic operation of X-GO membranes. Microspeaker sound pressure level response was measured in both time and frequency domains. X-GO diaphragms exhibit 45% higher damping than aluminum membranes in microspeaker assemblies. Consequently, X-GO membranes enable the upshift of loudspeaker breakup frequency by 1/6 to 1/2 octave above speakers assembled with oak wood, PET, aluminum and titanium membranes. GO based materials are thus found to be an exceptional material for electro-acoustic transduction.

Multi-label bird species classification from audio recordings using attention framework

For the conservation of avian biodiversity, bird detection is vital since it allows ornithologists to quantify which species exist in a particular area. Analyzing their acoustic signals enables the efficient identification of multiple bird species from overlapping recordings. This paper addresses classifying bird vocalizations in real-time audio recording using acoustic analysis. Schemes based on recurrent neural networks (RNN) are presented in the proposed work. Gated-recurrent units (GRU) are a particular type of RNN that has shown remarkable performance in acoustic classification. We propose a hierarchical Attention-based bidirectional gated recurrent unit (BiGRU) model for classifying acoustic signals of birds by using Mel-frequency cepstral coefficients (MFCC). The attention mechanism has proved its superior efficacy in many acoustic, speech and music processing applications. The attention mechanism is employed to give a different focus to the information outputted from the hidden layers of BiGRU. We adopted a short-time sliding-aggregation approach to decide on the test data, in which probability outcomes are species-wise summed and normalized. Species with the highest probability scores are assumed to be the dominant species in the recording. Our Attention-BiGRU classifier achieves pretty high performance in the Xeno-Canto dataset, with an F1-score of 0.84, with competing performance to the state-of-the-art multi-label classifiers.

Effective educational practices, assessment, and applications in acoustics and vibration at the University of Hartford.

The University of Hartford is home to two unique undergraduate engineering majors in acoustics, both sharing a core course layout of acoustics, vibrations, and projects. The Bachelor of Science in Mechanical Engineering with an Acoustics Concentration and the Bachelor of Science in Engineering in Acoustical Engineering and Music programs allow for two complementary tracks within the acoustics field, providing cohesive plans of study on many facets of listening and design. All Mechanical Engineering majors (regardless of concentration) are required to take Vibrations I and a course in Engineering and Environmental Acoustics. The department philosophy for this inclusion is that acoustics and vibration design considerations are an essential component for the development of the complete mechanical engineer. This paper outlines program educational goals and outcomes, along with pedagogical adjustments made based on continuous assessment and evaluation of select courses, including recent changes to adapt to measured deficiencies. The paper also details the historical development of the acoustics program, components of the Vibrations I and Engineering & Environmental Acoustics courses, and example research and design projects based on work in these courses. Among the included projects are modal analysis, community room acoustics assessment, and an open access computational room acoustics simulator for use and collaboration with colleagues in acoustics education.

Passive Bearing Estimation Using a 2-D Acoustic Vector Sensor Mounted on a Hybrid Autonomous Underwater Vehicle

The bearing estimation problem of low-frequency underwater acoustic sources is of paramount importance in antisubmarine warfare and passive acoustic monitoring applications. Typical solutions require manned vessels and towed arrays characterized by high costs, risks, and deployment difficulties. An alternative solution for such applications is represented by acoustic vector sensors (AVSs), which are lightweight, compact, and moderate in cost, have promising performance in terms of bearing discrimination and the potential to operate from autonomous underwater platforms. This article presents an experimental assessment of a 2-D AVS derived from a sonobuoy and mounted on a hybrid autonomous underwater vehicle (AUV) for estimating the bearing of underwater sources. The sensor model, its technical characteristics, the installation onboard the vehicle, and the implementation of the passive signal processing chain are reported. Three different algorithms for bearing estimation have been tested on real data: one based on a time-domain representation, one based on the frequency domain, and one relying on joint time–frequency representation. Experimentation and data are presented, first from very shallow waters within the Italian Navy base of CSSN, La Spezia, and then from the Tyrrhenian Sea, in the context of a multiplatform exercise led by the NATO S&TO CMRE. Using an acoustic navigation system as ground truth, these experiments allow the assessment of bearing estimation capabilities in both AUV (by using propeller and pump jets) and gliding navigation modes. While the results of all the three processing methods are at least par to those recently reported in similar experiments with different vector sensors and processing, the time–frequency-domain method can identify multiple sources without prior knowledge of the source signal waveform or bandwidth.

Semi-analytical estimation of Helmholtz resonators’ tuning frequency for scalable neck-cavity geometric couplings

Innovative meta-materials offer great flexibility for manipulating sound waves and assure unprecedented functionality in the context of acoustic applications. Indeed, they can exhibit extraordinary properties, such as broadband low-frequency absorption, excellent sound insulation, or enhanced sound transmission. These amazing properties have drawn the eye of the transport industry, especially for aeronautic applications where objects like these can be combined and coupled with primary structures aiming to reduce exterior and interior noise without increasing weight. However, the design of acoustic meta-materials with exciting functionality still represents a challenge, therefore there is a huge interest about the conceptualization and design of innovative acoustic solutions making use of meta-material resonance effects. The main target of the present research work is to obtain an accurate prediction of the tuning frequency of a Helmholtz-resonating device, whose resonance properties are exploited in a wide part of acoustic meta-material design. In this context, an investigation on a correction factor for the classical formulation used to estimate the Helmholtz resonance frequency starting from its geometric characteristics, accounting for different-shaped resonators with varying neck/cavity ratios, is performed. More specifically, a set of numerical simulations for several geometric configuration is considered in order to demonstrate the limits of pre-existing formulas, and a new correction factor formula is developed after theoretical considerations where it is possible. In the end, results in terms of correction factors are provided in both graphical and semi-analytical form, compared with Finite Element data.