Acoustic Transformation Research Articles

Ultrasonic in-process control of residual stresses in locomotive tires

The methods and means of controlling residual stresses in locomotive tires, developed on the basis of acoustoelasticity and involving electromagnetic acoustic transformation, are described. The original results of ultrasonic control of residual stresses in tires in the manufacture of wheel pairs for locomotives are given.

ACOUSTIC STRUCTURAL TESTING AND NONDESTRUCTIVE TESTING OF STEEL RODS (60C2A) IN THE PRODUCTION OF NANOSCALED STRUCTURE SPRINGS

The mechanisms of changing of key parameters of longitudinal and shear waves in springjsteel rods (60C2A) after different thermal treatment modes are described. The methods presented in the article provide high precise evaluation of such key parameters as wave speed and wave damping, efficiency of electromagnetic acoustic transformation which are used in assessment of elastic characteristics, thermal treatment quality and structure of material in the process of realization of electromagnetic acoustic repeated reflection method. Real flaw sensitivity of shadowgraph method based on repeated reflections under electromagnetic acoustic impulse and receiving of ultrasound tested on steel rods is demonstrated.

Modeling and Characterization of cMUT-based Devices Applied to Galvanic Isolation

This paper describes a new way of using cMUT technology: galvanic isolation for power electronics. These devices work like acoustic transformers, except that piezoelectricity is replaced by cMUT technology. Primary and secondary circuits are two cMUT-based transducers respectively layered on each side of a silicon substrate, through which the ultrasonic triggering signal is transmitted. A specific model based on a commercial finite element code was implemented to simulate these devices. A particular attention was paid on the modeling of the cMUT/substrate coupling which is a key feature for the intended application. First experimental results performed for model validation are presented here and discussed.

Research through the Stationary Specimen Method of the X20Cr13 Stainless Steel Used for Hydraulic Pumps

This paper presents the cavitation erosion research through the stationary specimen method of the X20Cr13 martensitic stainless steel. Actual research involves a number of 4 different tests for this material and the comparison between these experimental tests is highlighted by the images and graphs forwarded in the paper. All research was done using vibratory apparatus composed from electro-acoustic piezo-converter, acoustic transformer, a titanium sonotrode and the ultrasonic generator DG-2000. The mass loss of the martensitic stainless steel samples was measure with special digital balance.

Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects

In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialect-specific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to convert pronunciation of one dialect to another using learned rules, recognize dialects using learned rules, retrieve dialect-specific regions, and refine linguistic rules. Potential applications of our proposed framework include computer-assisted language learning, sociolinguistics, and diagnosis tools for phonological disorders.

Coal Gangue Interface Recognition Based on MFCC Research

In view of the existing coal gangue interface identification technology use ray method that it is not applicable to the working face what it dose not contain radioactive elements in the roof or contain low amounts of radioactive elements, and the detection range of radar detection is lesser, the signal attenuation is relatively serious, so putting forward a kind of coal gangue interface recognition based on Mel frequency cepstrum coefficient of MFCC. The method using coal gangue were put down in the process of coal gangue on the difference of the characteristics of the acoustic signal recognition, first of all, using Mel frequency cepstrum coefficient will after denoising coal gangue acoustic signal transformation to the frequency domain processing, extracting the 32 d characteristic parameters of coal and gangue acoustic signal;The experimental results show that the method can accurately identify the coal gangue down state.

Directivity control and efficiency of parametric loudspeakers with horns

This manuscript presents some recent results on directivity control and efficiency of parametric loudspeakers utilizing horns. Horns act both like an acoustic transformer and a directivity control method. An experimental device has been built and measurements have revealed that the horn has a clear influence on the audible sound levels and directivity of the parametric loudspeakers. When compared with a conventional megaphone, a much more flat frequency response is obtained and the resulting directivity is shown to be almost frequency independent.

Experience-dependent learning effects in speech production with spectrally degraded feedback

This study examined the speech of normal-hearing adult participants before and during their use of a portable, real-time vocoder (PRTV). The PRTV continuously transforms environmental acoustics, including speakers’ own speech feedback, via a real-time simulation of cochlear implant processing. The impacts of this substantial spectral degradation on speech production were measured in three groups of subjects: group 1 received altered acoustic feedback for one continuous 55 min session; group 2 experienced the feedback transformation for one session of 6 h total; and group 3 wore the PRTV for four consecutive sessions of 4 h each, for a total of 16 h of experience. Speakers in each group were recorded producing 114 isolated English words and 24 sentences both before their feedback manipulation began and at periodic intervals during their experimental session(s). Acoustic-phonetic analyses of the speech produced by subjects in all three groups revealed substantial effects of the spectral feedback degradation in several domains, including fluency/speaking rate, vocal affect, and vowel quality. Speakers were able to adjust and recover quickly in some of these areas, such as affect, while other changes, such as those in vowel quality and speaking rate, remained despite 16 h of experience with the acoustic transformation.

Highly tunable acoustic metamaterials based on a resonant tubular array

Recent theoretical studies demonstrated that waves can be steered in any desired fashion by using a suitable distribution of material parameters. However, the required parameters for acoustic transformations often surpass the ranges available from the metamaterials developed so far. We introduce a class of acoustic metamaterials based on standing waves in a tubular array and experimentally demonstrate continuous tuning of the compressibility in an unprecedentedly wide range from $\ensuremath{-}8$ to 6 relative to air at audio frequencies. Potential applications include the acoustic Luneburg lens and cloaking.

Large Eddy Simulations of Supersonic Impinging Jets

Supersonic impinging jet flow fields contain self-sustaining acoustic feedback features that create high levels of tonal noise. These types of flow fields are typically found with short takeoff and landing military aircraft as well as jet blast deflector operations on aircraft carrier decks. The United States Navy has a goal to reduce the noise generated by these impinging jet configurations and is investing in computational aeroacoustics to aid in the development of noise reduction concepts. In this paper, implicit large eddy simulation (LES) of impinging jet flow fields are coupled with a far-field acoustic transformation using the Ffowcs Williams and Hawkings (FW-H) equation method. The LES solves the noise generating regions of the flow and the FW-H transformation is used to predict the far-field noise. The noise prediction methodology is applied to a Mach 1.5 vertically impinging jet at a stand-off distance of five nozzle throat diameters. Both the LES and FW-H acoustic predictions compare favorably with experimental measurements. Time averaged and instantaneous flow fields are shown. A calculation performed previously at a stand-off distance of four nozzle throat diameters is revisited with adjustments to the methodology including a new grid, time integrator, and longer simulation runtime. The calculation exhibited various feedback loops which were not present before and can be attributed to an explicit time marching scheme. In addition, an instability analysis of the heated jets at both stand-off distances is performed. Tonal frequencies and instability modes are identified for the sample problems.

Acoustic analysis and feature transformation from neutral to whisper for speaker identification within whispered speech audio streams

Whispered speech is an alternative speech production mode from neutral speech, which is used by talkers intentionally in natural conversational scenarios to protect privacy and to avoid certain content from being overheard or made public. Due to the profound differences between whispered and neutral speech in vocal excitation and vocal tract function, the performance of automatic speaker identification systems trained with neutral speech degrades significantly. In order to better understand these differences and to further develop efficient model adaptation and feature compensation methods, this study first analyzes the speaker and phoneme dependency of these differences by a maximum likelihood transformation estimation from neutral speech towards whispered speech. Based on analysis results, this study then considers a feature transformation method in the training phase that leads to a more robust speaker model for speaker ID on whispered speech without using whispered adaptation data from test speakers. Three estimation methods that model the transformation from neutral to whispered speech are applied, including convolutional transformation (ConvTran), constrained maximum likelihood linear regression (CMLLR), and factor analysis (FA). a speech mode independent (SMI) universal background model (UBM) is trained using collected real neutral features and transformed pseudo-whisper features generated with the estimated transformation. Text-independent closed set speaker ID results using the UT-VocalEffort II corpus show performance improvement by using the proposed training framework. The best performance of 88.87% is achieved by using the ConvTran model, which represents a relative improvement of 46.26% compared to the 79.29% accuracy of the GMM-UBM baseline system. This result suggests that synthesizing pseudo-whispered speaker and background training data with the ConvTran model results in improved speaker ID robustness to whispered speech.

Constraint condition on transformation relation for generalized acoustics

For transformation acoustics (TA), the transformation relations for material and physical field are not unique when they are mapped from a virtual space to a physical space; the underlying mechanism is explored in this paper. We propose that the invariance of a physical process during a spatial mapping will impose the constraint condition for the transformation relation. This, together with the condition of energy conservation, provides a general method to derive the corresponding transformation relation for any physical process with the assumption of local affine transformation. When applied to TA, we show that the constraint conditions are not enough to determine the transformation relations completely, leaving a possibility to define them differently as found in the literature. New acoustic transformation relations with constant density or modulus are also proposed and validated numerically by constructing a two-dimensional acoustic cloak.

Negative refraction induced acoustic concentrator and the effects of scattering cancellation, imaging, and mirage

We present a three-dimensional acoustic concentrator capable of significantly enhancing the sound intensity in the compressive region with scattering cancellation, imaging, and mirage effects. The concentrator shell is built by isotropic gradient negative-index materials, which together with an exterior host medium slab constructs a pair of complementary media. The enhancement factor, which can approach infinity by tuning the geometric parameters, is always much higher than that of a traditional concentrator made by positive-index materials with the same size. The acoustic scattering theory is applied to derive the pressure field distribution of the concentrator, which is consistent with the numerical full-wave simulations. The inherent acoustic impedance match at the interfaces of the shell as well as the inverse processes of ``negative refraction---progressive curvature---negative refraction'' for arbitrary sound rays can exactly cancel the scattering of the concentrator. In addition, the concentrator shell can also function as an acoustic spherical magnifying superlens, which produces a perfect image with the same shape, with bigger geometric and acoustic parameters located at a shifted position. Then some acoustic mirages are observed whereby the waves radiated from (scattered by) an object located in the center region may seem to be radiated from (scattered by) its image. Based on the mirage effect, we further propose an intriguing acoustic transformer which can transform the sound scattering pattern of one object into another object at will with arbitrary geometric, acoustic, and location parameters.

Partially Supervised Speaker Clustering

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance.

Multilingual recognition of non-native speech using acoustic model transformation and pronunciation modeling

This article presents an approach for the automatic recognition of non-native speech. Some non-native speakers tend to pronounce phonemes as they would in their native language. Model adaptation can improve the recognition rate for non-native speakers, but has difficulties dealing with pronunciation errors like phoneme insertions or substitutions. For these pronunciation mismatches, pronunciation modeling can make the recognition system more robust. Our approach is based on acoustic model transformation and pronunciation modeling for multiple non-native accents. For acoustic model transformation, two approaches are evaluated: MAP and model re-estimation. For pronunciation modeling, confusion rules (alternate pronunciations) are automatically extracted from a small non-native speech corpus. This paper presents a novel approach to introduce confusion rules in the recognition system which are automatically learned through pronunciation modelling. The modified HMM of a foreign spoken language phoneme includes its canonical pronunciation along with all the alternate non-native pronunciations, so that spoken language phonemes pronounced correctly by a non-native speaker could be recognized. We evaluate our approaches on the European project HIWIRE non-native corpus which contains English sentences pronounced by French, Italian, Greek and Spanish speakers. Two cases are studied: the native language of the test speaker is either known or unknown. Our approach gives better recognition results than the classical acoustic adaptation of HMM when the foreign origin of the speaker is known. We obtain 22% WER reduction compared to the reference system. Furthermore, we take into account the written form of the spoken words: non-native speakers may rely on the writing of the words in order to pronounce them. This approach does not provide any further improvements.

On the efficiency of application of magnetic and electrical parameters for nondestructive testing of crystal-lattice microdistortions in heat-treated carbon steels

The effect of crystal-lattice microdistortions that characterize residual stresses in heat-treated steels that contain 0.36, 0.62, and 0.75% carbon on their magnetic characteristics, in particular, magnetic Barkhausen noise parameters, electrical resistivity, and elastic wave propagation velocity, which was determined by the electromagnetic acoustic transformation (EMAT) method, was studied. The coercive force and number of Barkhausen jumps for hardened steels are shown to correlate, to a great extent, with the average grain size rather than with crystal-lattice mirodistortions. Crystal-lattice microdistortions induced in steels upon hardening from different temperatures correlate adequately with the root-mean-square magnetic Barkhausen noise (RMS BN) voltage. When estimating crystallattice microdistortions in articles made from carbon steels subjected to marquenching and subsequent tempering, the combined use of the coercive force (for steels after low- and medium-temperature tempering) and RMS BN voltage (for steels after high-temperature tempering) is most efficient. Such parameters as the number of Barkhausen jumps, electrical resistivity, and elastic wave propagation velocity are less sensitive to changes in crystal-lattice microdistortions (CLMs) in heat-treated carbon steels.

Acoustic metamaterial exhibiting four different sign combinations of density and modulus

We fabricated a double negative acoustic metamaterial which consisted of Helmholtz resonators and membranes. Experimental data on the transmission and dispersion relation are presented. The system exhibited three frequencies where the acoustic state makes sharp transitions from density negative (ρ-NG) to double negative (DNG), modulus negative (B-NG), and double positive (DPS) in sequence with the frequency. We observed a wide range of negative refractive indexes from −0.06 to −3.7 relative to air, which will allow for new acoustic transformation techniques.

Are the products of statistical learning abstract or stimulus-specific?

Learners can segment potential lexical units from syllable streams when statistically variable transitional probabilities between adjacent syllables are the only cues to word boundaries. Here we examine the nature of the representations that result from statistical learning by assessing learners’ ability to generalize across acoustically different stimuli. In three experiments, we compare two possibilities: that the products of statistical segmentation processes are abstract and generalizable representations, or, alternatively, that products of statistical learning are stimulus-bound and restricted to perceptually similar instances. In Experiment 1, learners segmented units from statistically predictable streams, and recognized these units when they were acoustically transformed by temporal reversals. In Experiment 2, learners were able to segment units from temporally reversed syllable streams, but were only able to generalize in conditions of mild acoustic transformation. In Experiment 3, learners were able to recognize statistically segmented units after a voice change but were unable to do so when the novel voice was mildly distorted. Together these results suggest that representations that result from statistical learning can be abstracted to some degree, but not in all listening conditions.

Designing acoustic transformation devices using fluid homogenization of an elastic substructure

The design of devices using finite embedded coordinate transformations presents an unique approach to control acoustic waves. Though combining the use of conformal mappings may provide a pathway to more realizable material properties, many device geometries still require combinations of density and sound speed which are unavailable in isotropic materials. Here, we present a design strategy based on a multiple scattering homogenization method to approximate the unique values required within such a device. We apply the method, using full-wave simulations, to the design of an aqueous cylindrical-to-plane wave lens, which can be constructed from simple materials.

Dynamic Magnetoelastic Effects in the Fe<sub>80</sub>Si<sub>10</sub>B<sub>10</sub> Amorphous Ribbons Associated with Surface Crystallization

The dynamic magnetoelastic properties of the Fe80Si10B10 amorphous alloy at different stages of the thermal crystallization process were studied by the method of double electromagnetic acoustic transformation. An anomalous increase in the dynamical magnetostriction and ultrasound damping and a decrease of sound velocity were found. According to the Mössbauer spectroscopy data, they are related to the formation of a component of magnetic moments that is perpendicular to the ribbon surface.