Transient Acoustic Signals Research Articles

Computing where ocean fluctuations affect the acoustic impulse response

We compute accurate maps of oceanic fluctuations affecting transient acoustic signals propagating between a source and receiver. The technological advance involves coupling the One-Way Wave Equation (OWWE) propagation model with the theory for the Differential Measure of Influence (DMI). This theory uses two finite-frequency solutions of the linear acoustic wave equation: from source to receiver and vice versa. They must obey reciprocity. OWWE satisfies reciprocity with great accuracy even at basin-scales when the field of sound speed varies horizontally and vertically. At infinite frequency, maps of the DMI collapse into infinitely thin rays. Mapping the DMI is useful for understanding measurements of the amplitude and travel time changes of sound at finite frequencies. [Work supported by ONR contracts N00014-12-C0230 and N62909-12-1-7033.]

Characterization of space dust using acoustic impact detection.

This paper describes studies leading to the development of an acoustic instrument for measuring properties of micrometeoroids and other dust particles in space. The instrument uses a pair of easily penetrated membranes separated by a known distance. Sensors located on these films detect the transient acoustic signals produced by particle impacts. The arrival times of these signals at the sensor locations are used in a simple multilateration calculation to measure the impact coordinates on each film. Particle direction and speed are found using these impact coordinates and the known membrane separations. This ability to determine particle speed, direction, and time of impact provides the information needed to assign the particle's orbit and identify its likely origin. In many cases additional particle properties can be estimated from the signal amplitudes, including approximate diameter and (for small particles) some indication of composition/morphology. Two versions of this instrument were evaluated in this study. Fiber optic displacement sensors are found advantageous when very thin membranes can be maintained in tension (solar sails, lunar surface). Piezoelectric strain sensors are preferred for thicker films without tension (long duration free flyers). The latter was selected for an upcoming installation on the International Space Station.

Observations and Bayesian location methodology of transient acoustic signals (likely blue whales) in the Indian Ocean, using a hydrophone triplet.

A notable sequence of calls was encountered, spanning several days in January 2003, in the central part of the Indian Ocean on a hydrophone triplet recording acoustic data at a 250 Hz sampling rate. This paper presents signal processing methods applied to the waveform data to detect, group, extract amplitude and bearing estimates for the recorded signals. An approximate location for the source of the sequence of calls is inferred from extracting the features from the waveform. As the source approaches the hydrophone triplet, the source level (SL) of the calls is estimated at 187 ± 6 dB re: 1 μPa-1 m in the 15-60 Hz frequency range. The calls are attributed to a subgroup of blue whales, Balaenoptera musculus, with a characteristic acoustic signature. A Bayesian location method using probabilistic models for bearing and amplitude is demonstrated on the calls sequence. The method is applied to the case of detection at a single triad of hydrophones and results in a probability distribution map for the origin of the calls. It can be extended to detections at multiple triads and because of the Bayesian formulation, additional modeling complexity can be built-in as needed.

A follow up low frequency propagation experiment in Currituck Sound

An initial low frequency propagation experiment was conducted in September 2013 in which a Combustive Sound Source (CSS) generated transient acoustic signals in water depths of approximately 2.5 m [Costley et al. J. Acoust. Soc. Am. 136, 2178 (2014)]. High amplitude signals were generated mid-water column and recorded with bottom mounted hydrophones at distances up to 200 m. A time harmonic, two-dimensional axisymmetric finite element model under-predicted the magnitudes of the recorded pressure signals. A follow up experiment was conducted in October 2014. In addition to using the CSS and the bottom mounted hydrophones, a 4-element vertical line array was deployed. Measurements were made at source-receiver separations from approximately 10 m to 1000 m in 100 m increments. Additionally, acoustic properties of the sediment were obtained through in-situ measurements [McNeese et al. J. Acoust. Soc. Am. 136, 2252 (2014)]. The results of the experiment, along with results of the finite element model using updated sediment properties, will be presented. [Work supported by the U.S. Army Engineer Research and Development Center. Permission to publish was granted by Director, Geotechnical & Structures Laboratory.]

Graph-based sensor fusion for classification of transient acoustic signals.

Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e., rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features in terms of robustness to signal distortions. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as the recently proposed joint sparsity models for multisensor acoustic classification. Additionally our proposed algorithm is less sensitive to insufficiency in training samples compared to competing approaches.

Comparison of Young's Modulus Property Determination of Metallic Materials under Two Statistical Analysis Methods

This paper presents an alternative statistical signal analysis method to characterise and determine Youngs modulus property of metallic materials. For this characterisation purpose, we propose an alternative method called Integrated Kurtosis-based Algorithm for Z-notch filter (I-kazTM) and Mesokurtosis Zonal Nonparametric (M-Z-N). Impulsive excitation test has been performed according to ASTM E1876 on three metallic materials of medium carbon steel S50C, stainless steel AISI 304 and brass to measure the piezoelectric and acoustic signals. The transient acoustic signal has been analysed using M-Z-N analysis while I-kazTM has been used to analyse the impulsive piezoelectric signal. Correlation expression between Youngs modulus property and the calculated statistical parameters has been discussed and the accuracy of these two methods has been identified using cast iron FCD 500 specimen.

Identify on Acoustic Emission Signals of Tank Bottom Corrosion Using Hidden Markov Model

Aiming at the acoustic emission signals of oil storage tank bottom injured, hidden Markov algorithm is proposed to identify the tank bottom corrosion signal. Typical corrosion acoustic emission signal is divided into transient acoustic signal, continuous acoustic emission signal and mixed acoustic emission.Baum-Welch algorithm is used to train these typical corrosion acoustic emission signals model, then establish HMM model library. The forward-backward algorithm is used to compute each acoustic emission model’s output probability. The simulation experiments shows that the hidden Markov algorithm can correctly identified the acoustic emission signals.

Exploiting correlation in wind noise for enhanced detection of transient acoustic signals

Wind noise presents significant difficulties when trying to detect transient acoustic signals. The most common approach to enhancing signal detection when the signal-to-noise ratio is low due to wind noise is to utilize mechanical windscreens, large number of widely spaced microphones or a combination of both. Results from recent experimental investigations and algorithm developments are presented that demonstrate a alternative method for improving detection of transients that utilizes only a few closely spaced microphones and a unique processing technique that explicitly exploits the correlation among wind noise induced pressure fluctuations.

Piecewise coherent mode processing of acoustic data recorded on two horizontally separated vertical line arrays

Motivated by measurements made in the 2004 Long-Range Ocean Acoustic Propagation Experiment (LOAPEX), the problem of mode processing transient acoustic signals collected on two nearby vertical line arrays is considered. The first three moments (centroid, variance, and skewness) of broadband distributions of acoustic energy with fixed mode number (referred to as modal group arrivals) are estimated. It is shown that despite the absence of signal coherence between the two arrays and poor high mode number energy resolution, the centroid and variance of these distributions can be estimated with tolerable errors using piecewise coherent mode processing as described in this paper.

Joint-Structured-Sparsity-Based Classification for Multiple-Measurement Transient Acoustic Signals

This paper investigates the joint-structured-sparsity-based methods for transient acoustic signal classification with multiple measurements. By joint structured sparsity, we not only use the sparsity prior for each measurement but we also exploit the structural information across the sparse representation vectors of multiple measurements. Several different sparse prior models are investigated in this paper to exploit the correlations among the multiple measurements with the notion of the joint structured sparsity for improving the classification accuracy. Specifically, we propose models with the joint structured sparsity under different assumptions: same sparse code model, common sparse pattern model, and a newly proposed joint dynamic sparse model. For the joint dynamic sparse model, we also develop an efficient greedy algorithm to solve it. Extensive experiments are carried out on real acoustic data sets, and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.

Knee acoustic emission: A potential biomarker for quantitative assessment of joint ageing and degeneration

Based on a single time-point study of 34 healthy and 19 osteoarthritic knees in three different age groups (early, middle and late adulthood), this paper reports the potential of knee acoustic emission as a biomarker to monitor joint ageing and degeneration. Measurements were made of short transient high frequency acoustic emission signals generated by knee joints under stress during repeated sit–stand–sit movements along with joint angle. A statistically significant feature profile was established using a four-phase model of sit–stand–sit movements and two waveform features. The four-phase movement model is derived from joint angle measurement during repeated sit–stand–sit movements, and it consists of the ascending-acceleration and ascending-deceleration phases in the sit-to-stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. The two statistically significant waveform features are extracted from AE measurement during repeated sit–stand–sit movements, and they consist of the peak magnitude value and average signal level of each AE burst. In addition to the use of bilateral plots, statistical distributions and 2D colour histograms to visualise the differences and similarities among participants, use of principal component analysis showed not only distinct data clusters corresponding to participating groups, but also an age- and disease-related trajectory progressing from the early adulthood healthy group to the late adulthood healthy group followed by the middle adulthood osteoarthritic group to the late adulthood osteoarthritic group. Furthermore, this trajectory shows increasing areas for each data cluster, with a highly compact cluster for the early adulthood healthy group at one end and a widely spread cluster for the late adulthood osteoarthritic group at the other end. From these results, a strong basis is formed for further development of knee acoustic emission as a convenient and non-invasive biomarker for quantitative assessment of joint ageing and degeneration.

Localization of Antarctic ice breaking events by frequency dispersion of the signals received at a single hydroacoustic station in the Indian Ocean

Transient acoustic signals from Antarctic ice cracking and breaking events, featuring significant frequency dispersion, were observed at the hydroacoustic stations deployed in the Indian Ocean as part of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty. Based on a comparison with numerical predictions, the measured dispersion characteristics were used to estimate the range between ice events and the receiver. Combined with the bearing capability of the IMS stations, these estimates allow us to locate ice events from a single hydroacoustic station. An analysis of range estimation errors due to uncertainty of the measured time-frequency structure of signal arrivals and due to variations of the sound speed profiles was also conducted. The analysis showed that the location accuracy from a single hydroacoustic station for the typical ice events was of the same order, as that determined from an intersection of bearings from two remote stations, if the signal frequency bandwidth is as wide as at least 5 Hz and lies within 5 Hz to 35 Hz frequency range. This localization method was examined by analysing several ice events detected at both the Cape Leeuwin IMS station and a sea noise logger deployed off the Antarctic shelf.

A frequency domain laser based ultrasonic system for time resolved measurement of broadband acoustic transients

A high-sensitivity frequency domain laser based ultrasonic system is presented which uses a low power, amplitude modulated continuous wave (cw) laser source for acoustic wave generation. The acoustic signals are detected using a path stabilized Michelson interferometer coupled to a rf lock-in amplifier. The modulation frequency of the generation laser is scanned over the bandwidth of interest, and transient acoustic signals are reconstructed from the frequency domain data. The effects of measurement frequency resolution, bandwidth, and time domain aliasing on the reconstructed transient response are discussed. Experimental results on thin plates, where diffuse acoustic wave fields lasting several hundred microseconds are seen as a result of multiple reflections off of sample boundaries, demonstrate that the time domain signal can be unambiguously reconstructed through appropriate selection of frequency resolution. Time domain reconstructions of acoustic signals over a bandwidth of 200MHz demonstrate the utility of the approach for acoustic microscopy; individual acoustic transients can be detected with extremely high signal to noise ratio and time gated for analysis.

Time-Frequency Analysis Using Warped-Based High-Order Phase Modeling

The high-order ambiguity function (HAF) was introduced for the estimation of polynomial-phase signals (PPS) embedded in noise. Since the HAF is a nonlinear operator, it suffers from noise-masking effects and from the appearance of undesired cross-terms when multicomponents PPS are analyzed. In order to improve the performances of the HAF, the multi-lag HAF concept was proposed. Based on this approach, several advanced methods (e.g., product high-order ambiguity function (PHAF)) have been recently proposed. Nevertheless, performances of these new methods are affected by the error propagation effect which drastically limits the order of the polynomial approximation. This phenomenon acts especially when a high-order polynomial modeling is needed: representation of the digital modulation signals or the acoustic transient signals. This effect is caused by the technique used for polynomial order reduction, common for existing approaches: signal multiplication with the complex conjugated exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the polynomial order, based on the successive unitary signal transformation, according to each polynomial order. We will prove that this method reduces considerably the effect of error propagation. Namely, with this order reduction method, the estimation error at a given order will depend only on the performances of the estimation method.

Adaptive window-length detection of underwater transients using wavelets

This paper describes a detection method that adapts to unknown characteristics of the underlying transient signal, such as location, length, and time-frequency content. It applies a set of embedded detectors tuned to a number of signal partitions. The detectors are based on the wavelet theory, whereby two different techniques are examined, one using local Fourier transform and the other using discrete wavelet transform. The detection statistics are computed so as to enable prewhitening of unknown colored noise and to allow for a constant false-alarm rate detection. An adapted segmentation of the signal is next obtained with a goal of finding the largest detection statistics within each segment of the partition. The detectors are tested using several underwater acoustic transient signals buried in ambient sea noise.

Sensor Position Estimation and Source Ranging in a Shallow Water Environment

Underwater acoustic transient signals are generated mechanically at known positions along a wharf. These signals are received by a wide aperture planar array of four underwater acoustic sensors, whose positions relative to the wharf are unknown. A method is described that enables the positions of the sensors to be estimated from accurate differential time-of-arrival measurements (with 0.1 /spl mu/s precision) as the signal wavefronts traverse the array. A comparison of the estimated positions with the nominal positions of the first three sensors, which form a 20-m-wide aperture horizontal line array, reveals a 2-cm displacement of the middle sensor from the line array axis. This slight bowing of the line array results in overranging (bias error of 3%) when the wavefront curvature method is used with the nominal collinear sensor positions to locate a static source of active sonar transmissions at a range of 59.2 m. The use of the spherical intersection method coupled with the estimated sensor positions of the line array provides an order of magnitude improvement in the range estimate (within 0.3% of the actual value). However, systematic ranging errors are observed when the sound propagation medium becomes nonstationary. Next, the differences in the arrival times of the direct path and boundary-reflected path signals at the middle sensor of the wide aperture line array are estimated using the differential phase residue of the analytic signal at the sensor output. These multipath delays are used to estimate the range and depth of the source. Although the average value of the multipath range estimates is within 0.5% of the actual value, the variance of the range estimates is 50 times larger when compared with the results of the spherical intersection and wavefront curvature methods. The multipath delay data are also processed to provide a reliable estimate of the temporal variation in the water depth enabling the tidal variation to be observed.

Structural integrity monitoring of steam generator tubing using transient acoustic signal analysis

The integrity diagnosis of tubular structures is studied in this research using guided acoustic waves. A smart sensor array is deployed to activate and collect Lamb wave signals that propagate along metallic tubes. Several advanced techniques have been explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang Transform (HHT) is a recently developed technique for nonlinear nonstationary signal processing. A moving window is introduced to generate the local peak information from transient series, and a zooming window algorithm is developed to localize the structural flaws. The extracted features using moving windows are utilized for classifying structural defects in brass tubes. An experimental piezo-transducer system has been developed for testing several types of flaws in brass tubes in air and in water, and to demonstrate the implementation of HHT for flaw detection, localization, and classification. The techniques are also shown to be effective under background/process noise. This approach has the potential of on-line integrity monitoring of tubes in steam generators, boilers, and heat exchangers, used in process and power plants.

Reducing ambient noise in time series data with the Bartlett spectrogram

A signal processing technique for enhancing the signal-to noise ratio of transient acoustic signals is analyzed. This method subtracts the average power spectral density as calculated from the Bartlett spectrogram from the data in the frequency domain, and then forms a new time series from the resultant spectrogram and original phase information. We examine signal-to-noise ratio improvements that occur. Noisy time-series containing transient signals from synthetic data and real marine mammal recordings are used as test data sets. Results show that improvements of around 10 dB can be achieved theoretically. Limitations and pre-application considerations also are discussed.

Passive ranging errors due to multipath distortion of deterministic transient signals with application to the localization of small arms fire.

A passive ranging technique based on wavefront curvature is used to estimate the ranges and bearings of impulsive sound sources represented by small arms fire. The discharge of a firearm results in the generation of a transient acoustic signal whose energy propagates radially outwards from the omnidirectional source. The radius of curvature of the spherical wavefront at any instant is equal to the instantaneous range from the source. The curvature of the acoustic wavefront is sensed with a three-microphone linear array by first estimating the differential time of arrival (or time delay) of the acoustic wavefront at each of the two adjacent sensor pairs and then processing the time-delay information to extract the range and bearing of the source. However, modeling the passive ranging performance of the wavefront curvature method for a deterministic transient signal source in a multipath environment shows that when the multipath and direct path arrivals are unresolvable, the time-delay estimates are biased which, in turn, biases the range estimates. The model explains the observed under-ranging of small arms firing positions during a field experiment.

Wavelet packets and de-noising based on higher-order-statistics for transient detection

In this paper, we present a detector of transient acoustic signals that combines two powerful detection tools: a local wavelet analysis and higher-order statistical properties of the signals. The use of both techniques makes detection possible in low signal-to-noise ratio conditions, when other means of detection are no longer sufficient. The proposed algorithm uses the adapted wavelet packet transform. It leads to a partition of the signal which is ‘optimal’ according to a criterion that tests the Gaussian nature of the frequency bands. To get a time dependent detection curve, we perform a de-noising procedure on the wavelet coefficients: The Gaussian coefficients are set to zero. We then apply a classical method of detection on the time reconstructed de-noised signal. We study the performance of the detector in terms of experimental ROC curves. We show that the detector performs better than decompositions using other classical splitting criteria. In the last part, we present an application of the algorithm on real flow recordings of nuclear plant pipings. The detector indicates the presence of a missing body in the piping at some instants not seen with a classical energy detector.