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In active noise control (ANC) systems, virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear ANC algorithms, such as the filtered-x least mean square (FXLMS) algorithm, have been used with virtual sensing techniques. In this paper, a nonlinear ANC algorithm is developed for a virtual microphone by integrating the remote microphone technique with the filtered-s least mean square (FSLMS) algorithm. The proposed algorithm is evaluated experimentally in the cancellation of chaotic noise in a one-dimensional duct. The secondary paths evaluated experimentally exhibit non-minimum phase response and hence poor performance is obtained with the conventional FXLMS algorithm compared to the proposed FSLMS based algorithm. This is because the latter is capable of predicting the chaotic signal found in many physical processes responsible for noise. In addition, the proposed algorithm is shown to outperform the FXLMS based remote microphone technique under the causality constraint (when the propagation delay of the secondary path is greater than the primary path). A number of experimental results are presented in this paper to compare the performance of the FSLMS algorithm based virtual ANC algorithm with the FXLMS based virtual ANC algorithm. [ABSTRACT FROM AUTHOR]

The first aim of this paper is to give emphasis to the importance of assessing phase information when reconstructing and mapping a sound field. In fact, in acoustic analysis phase distribution is frequently simply either not considered or ignored, even though it can supply very useful information for the understanding of the mapping itself or for further analyses. In this paper a procedure to carry out phase mapping of acoustic sources in beamforming measurement is illustrated. The second aim of this paper is to propose a straightforward iterative optimization algorithm based on the monopole substitution starting from beamforming results. It allows for the reconstruction of sound field without the use of any matrix inversion. Both numerical and experimental validations of the method are presented. Results shown hereafter prove the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Recently, a computational algorithm based on Bayesian data analysis was presented to invert the linear rheology of branched polymers [Shanbhag, S., Rheol. Acta 49, 411-422 (2010)]. When rheological data of an unknown polymer mixture are supplied, the algorithm produces an exhaustive distribution of structures and compositions, consistent with the rheology. Frequently, it identifies multiple or degenerate structures. In this proof-of-concept paper, a resolution of the degeneracy is sought by appealing to the concept of combinatorial rheology [Larson, R. G., Macromolecules 34, 4556-4571 (2001)], where the unknown sample is strategically blended with a well-characterized fraction. Experimental load is alleviated by identifying the optimal type, molecular weight, and composition of the polymer fraction to blend with the unknown sample, to discriminate between the degenerate structures, most conclusively. Two methods are proposed and tested on a particular mixture that is characterized by severe degeneracy. Both proposed methods are able to correctly identify the structure and composition of the original mixture by requiring two additional rheological experiments. [ABSTRACT FROM AUTHOR]

An equivalent fluid model based finite-difference time-domain algorithm is proposed to simulate the frequency characteristic of porous material with rigid frame. The rigid-frame porous material is replaced by an equivalent fluid characterized by the effective density and the effective bulk modulus, which reflect the viscous effects and thermal effects in the porous material, respectively. The effective density and the effective bulk modulus are frequency-dependent complex values, which are designed in the form of infinite impulse response filters in the frequency domain. The Z transform theory is used to discretize the frequency-domain wave equations. The accuracy of the proposed algorithm is preliminarily validated by good agreement between the calculated and measured normal-incidence sound absorption coefficients of two samples composed of different multiple-layered materials in a wide frequency range. [ABSTRACT FROM AUTHOR]

Objective measures are commonly used in the development of speech coding algorithms as an adjunct to human subjective evaluation. Predictors of speech quality based on models of physiological or perceptual processing tend to perform better than measures based on simple acoustical properties. Here, a modeling method based on a detailed physiological model and a neurogram similarity measure is developed and optimized to predict the quality of an enhanced wideband speech dataset. A model capturing temporal modulations in neural activity up to 267 Hz was found to perform as well as or better than several existing objective quality measures. [ABSTRACT FROM AUTHOR]

The high frequency ambient noise in warm shallow waters is dominated by snapping shrimp. The loud snapping noises they produce are impulsive and broadband. As the noise propagates through the water, it interacts with the seabed, sea surface, and submerged objects. An array of acoustic pressure sensors can produce images of the submerged objects using this noise as the source of acoustic 'illumination.' This concept is called ambient noise imaging (ANI) and was demonstrated using ADONIS, an ANI camera developed at the Scripps Institution of Oceanography. To overcome some of the limitations of ADONIS, a second generation ANI camera (ROMANIS) was developed at the National University of Singapore. The acoustic time series recordings made by ROMANIS during field experiments in Singapore show that the ambient noise is well modeled by a symmetric α-stable (SαS) distribution. As high-order moments of SαS distributions generally do not converge, ANI algorithms based on low-order moments and fractiles are developed and demonstrated. By localizing nearby snaps and identifying the echoes from an object, the range to the object can be passively estimated. This technique is also demonstrated using the data collected with ROMANIS. [ABSTRACT FROM AUTHOR]

Head-related transfer functions (HRTFs) measured from human subjects were approximated using infinite-impulse-response (IIR) filter models. Models were restricted to rational transfer functions (plus simple delays) so that specific models are characterized by the locations of poles and zeros in the complex plane. The all-pole case (with no nontrivial zeros) is treated first using the theory of linear prediction. Then the general pole-zero model is derived using a weighted-least-squares (WLS) formulation of the modified least-squares problem proposed by Kalman (1958). Both estimation algorithms are based on solutions of sets of linear equations and result in efficient computational schemes to find low-order model HRTFs. The validity of each of these two low-order models was assessed in psychophysical experiments. Specifically, a four-interval, two-alternative, forced-choice paradigm was used to test the discriminability of virtual stimuli constructed from empirical and model HRTFs for corresponding locations. For these experiments, the stimuli were 80 ms, noise tokens generated from a wideband noise generator. Results show that sounds synthesized through model HRTFs were indistinguishable from sounds synthesized from original HRTF measurements for the majority of positions tested. The advantages of the techniques described here are the computational efficiencies achieved for low-order IIR models. Properties of the all-pole and pole-zero estimators are discussed in the context of low-order HRTF representations, and implications for basic and applied contexts are considered. [ABSTRACT FROM AUTHOR]