Acoustic Source - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Acoustic Source

The Experts below are selected from a list of 61509 Experts worldwide ranked by ideXlab platform

Acoustic Source – Free Register to Access Experts & Abstracts

Gary W Elko – One of the best experts on this subject based on the ideXlab platform.

  • method and apparatus for passive Acoustic Source localization for video camera steering applications
    Journal of the Acoustical Society of America, 2005
    Co-Authors: Jacob Benesty, Gary W Elko, Yiteng Huang

    Abstract:

    A real-time passive Acoustic Source localization system for video camera steering advantageously determines the relative delay between the direct paths of two estimated channel impulse responses. The illustrative system employs an approach referred to herein as the “adaptive eigenvalue decomposition algorithm” (AEDA) to make such a determination, and then advantageously employs a “one-step least-squares algorithm” (OSLS) for purposes of Acoustic Source localization, providing the desired features of robustness, portability, and accuracy in a reverberant environment. The AEDA technique directly estimates the (direct path) impulse response from the sound Source to each of a pair of microphones, and then uses these estimated impulse responses to determine the time delay of arrival (TDOA) between the two microphones by measuring the distance between the first peaks thereof (i.e., the first significant taps of the corresponding transfer functions). In one embodiment, the system minimizes an error function (i.e., a difference) which is computed with the use of two adaptive filters, each such filter being applied to a corresponding one of the two signals received from the given pair of microphones. The filtered signals are then subtracted from one another to produce the error signal, which is minimized by a conventional adaptive filtering algorithm such as, for example, an LMS (Least Mean Squared) technique. Then, the TDOA is estimated by measuring the “distance” (i.e., the time) between the first significant taps of the two resultant adaptive filter transfer functions.

  • passive Acoustic Source localization for video camera steering
    International Conference on Acoustics Speech and Signal Processing, 2000
    Co-Authors: Yiteng Huang, Jacob Benesty, Gary W Elko

    Abstract:

    A multi-input one-step least-squares (OSLS) algorithm for passive Source localization is proposed. It is shown that the OSLS algorithm is mathematically equivalent to the so-called spherical interpolation (SI) method but with less computational complexity. The OSLS/SI method uses spherical equations (instead of hyperbolic equations) and solves them in a least-squares sense. Based on the adaptive eigenvalue decomposition time delay estimation method previously proposed by the same authors and the OSLS Source localization algorithm, a real-time passive Source localization system for video camera steering is presented. The system demonstrates many desirable features such as accuracy, portability, and robustness.

  • adaptive eigenvalue decomposition algorithm for real time Acoustic Source localization system
    International Conference on Acoustics Speech and Signal Processing, 1999
    Co-Authors: Yi Huang, Jacob Benesty, Gary W Elko

    Abstract:

    To locate an Acoustic Source in a room, the relative delay between microphone pairs must be determined efficiently and accurately. However, most traditional time delay estimation (TDE) algorithms fail in reverberant environments. A new approach is proposed that takes into account the reverberation of the room. A real time PC-based TDE system running under Microsoft/sup TM/ Windows system was developed with three TDE techniques: classical cross-correlation, phase transform, and a new algorithm that is proposed in this paper. The system provides an interactive platform that allows users to compare performance of these algorithms.

Tribikram Kundu – One of the best experts on this subject based on the ideXlab platform.

  • Acoustic Source localization on the surface of a cylindrical pressure vessel
    Health Monitoring of Structural and Biological Systems IX, 2020
    Co-Authors: Zhiwen Cui, Shenxin Yin, Tribikram Kundu

    Abstract:

    The safety of pressure vessels has been a concern in recent years. Old pressure vessels are susceptible to failure due to fatigue damage after several years of usage. Fire and spill of hazardous liquid or gas due to pressure vessel failure can cost human life and cause property damage. Therefore, monitoring these critical structures has become a problem of considerable interest. Nondestructive testing and structural health monitoring play an important role for both manufacturing and periodic inspection of pressure vessels. Acoustic emission technology is a popular and widely used technology for pressure vessel monitoring. The Acoustic Source localization (ASL) technique developed for the two-dimensional planar structures is applied to the surface of a cylindrical pressure vessel. The ASL on the cylindrical pressure vessel surface is performed by the time difference of arrival (TDOA) method without knowing the Acoustic properties of the material. In the experiment six sensors are placed in two clusters. The location of the Acoustic Source is unknown, and the arrival time of the Acoustic signal to each sensor is measured. After analyzing the measured data as discussed in the paper one can calculate the Acoustic Source position. The method is experimentally verified. The results show that the above technique can quickly and accurately locate the Acoustic Source position on the surface of a cylindrical pressure vessel without having the complete knowledge of the structural properties of the material.

  • Acoustic Source localization in a highly anisotropic plate with unknown orientation of its axes of symmetry and material properties with numerical verification
    Ultrasonics, 2019
    Co-Authors: Novonil Sen, Tribikram Kundu

    Abstract:

    Abstract Development of Acoustic Source localization techniques in anisotropic plates has gained attention in the recent past and still has scope of improvement. Most of such techniques existing in the literature either require known material properties or assume a straight line propagation of wave energy from the Acoustic Source to a sensor. These limitations have been overcome in recent years by employing wave front shape-based techniques. However, the existing wave front shape-based techniques are applicable in situations where the orientation of the axes of symmetry of the anisotropic plate is known beforehand. In the present study, a modified version of these techniques, namely, elliptical and parametric curve-based techniques, is proposed. This new version is useful when the angle between the axes of symmetry and the reference Cartesian coordinate system is unknown. In the new definition of the objective function, the orientation of the axes of symmetry of the anisotropic plate is treated as an input in the objective function in addition to the other unknowns like the Source coordinates and the curve parameters. A numerical study illustrates how the modified new techniques can localize the Acoustic Source with sufficient accuracy in an anisotropic plate with unknown orientation of the axes of symmetry and its material properties.

  • Acoustic Source localization in heterogeneous media.
    Ultrasonics, 2019
    Co-Authors: Jia Fu, Tribikram Kundu

    Abstract:

    Abstract Acoustic Source localization (ASL) or predicting the location of the Acoustic Source in a structure by analyzing the recorded signals at the receivers is of considerable interest for various applications. Recent research advances on this topic have been limited to homogeneous media. This paper presents a solution for Acoustic Source localization in a heterogeneous medium without knowing the properties of different materials constituting the heterogeneous structure. In this paper new developments for Acoustic Source localization in an anisotropic plate is first reviewed briefly. Then an ASL technique is presented for localizing Acoustic Source in heterogeneous layered structures when the layer properties are not known. The proposed technique is verified experimentally and numerically. The experimental results were generated with a specimen having one interface while numerical results were generated for both two-layered medium with one interface and three-layered medium with two interfaces.

Jacob Benesty – One of the best experts on this subject based on the ideXlab platform.

  • method and apparatus for passive Acoustic Source localization for video camera steering applications
    Journal of the Acoustical Society of America, 2005
    Co-Authors: Jacob Benesty, Gary W Elko, Yiteng Huang

    Abstract:

    A real-time passive Acoustic Source localization system for video camera steering advantageously determines the relative delay between the direct paths of two estimated channel impulse responses. The illustrative system employs an approach referred to herein as the “adaptive eigenvalue decomposition algorithm” (AEDA) to make such a determination, and then advantageously employs a “one-step least-squares algorithm” (OSLS) for purposes of Acoustic Source localization, providing the desired features of robustness, portability, and accuracy in a reverberant environment. The AEDA technique directly estimates the (direct path) impulse response from the sound Source to each of a pair of microphones, and then uses these estimated impulse responses to determine the time delay of arrival (TDOA) between the two microphones by measuring the distance between the first peaks thereof (i.e., the first significant taps of the corresponding transfer functions). In one embodiment, the system minimizes an error function (i.e., a difference) which is computed with the use of two adaptive filters, each such filter being applied to a corresponding one of the two signals received from the given pair of microphones. The filtered signals are then subtracted from one another to produce the error signal, which is minimized by a conventional adaptive filtering algorithm such as, for example, an LMS (Least Mean Squared) technique. Then, the TDOA is estimated by measuring the “distance” (i.e., the time) between the first significant taps of the two resultant adaptive filter transfer functions.

  • passive Acoustic Source localization for video camera steering
    International Conference on Acoustics Speech and Signal Processing, 2000
    Co-Authors: Yiteng Huang, Jacob Benesty, Gary W Elko

    Abstract:

    A multi-input one-step least-squares (OSLS) algorithm for passive Source localization is proposed. It is shown that the OSLS algorithm is mathematically equivalent to the so-called spherical interpolation (SI) method but with less computational complexity. The OSLS/SI method uses spherical equations (instead of hyperbolic equations) and solves them in a least-squares sense. Based on the adaptive eigenvalue decomposition time delay estimation method previously proposed by the same authors and the OSLS Source localization algorithm, a real-time passive Source localization system for video camera steering is presented. The system demonstrates many desirable features such as accuracy, portability, and robustness.

  • adaptive eigenvalue decomposition algorithm for passive Acoustic Source localization
    Journal of the Acoustical Society of America, 2000
    Co-Authors: Jacob Benesty

    Abstract:

    To find the position of an Acoustic Source in a room, the relative delay between two (or more) microphone signals for the direct sound must be determined. The generalized cross-correlation method is the most popular technique to do so and is well explained in a landmark paper by Knapp and Carter. In this paper, a new approach is proposed that is based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the microphone signals contains the impulse responses between the Source and the microphone signals (and therefore all the information we need for time delay estimation). In experiments, the proposed algorithm performs well and is very accurate.