The Experts below are selected from a list of 27786 Experts worldwide ranked by ideXlab platform
William A. Yost - One of the best experts on this subject based on the ideXlab platform.
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History of sound Source Localization: 1850-1950
2017Co-Authors: William A. YostAbstract:While scientists and philosophers have been interested in sound Source Localization since the time of the ancient Greeks, the modern study of this topic probably began in the late 19th century. Because sound has no spatial dimensions, there were many arguments at this time as to how humans localize a Source based on the sound it produces. Lord Rayleigh conducted a “garden experiment” and concluded that a binaural ratio of sound level at each ear could account for his ability to identify the location of people who spoke in the garden. This type of experiment began the modern investigation of the acoustic cues used for sound Source Localization. In the first half of the 20th century, psychoacousticians such as Licklider, Jeffress, Mills, Newman, Rosenzweig, Stevens, von Hornbostel, Wallach, Wertheimer, and many others (documented by Boring in Sensation and Perception, 1942 and by Blauert in Spatial Hearing, 1997) added seminal papers leading to our current understanding of sound Source Localization. This pr...
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Sound Source Localization identification accuracy: Envelope dependencies
Journal of the Acoustical Society of America, 2017Co-Authors: William A. YostAbstract:Sound Source Localization accuracy as measured in an identification procedure in a front azimuth sound field was studied for click trains, modulated noises, and a modulated tonal carrier. Sound Source Localization accuracy was determined as a function of the number of clicks in a 64 Hz click train and click rate for a 500 ms duration click train. The clicks were either broadband or high-pass filtered. Sound Source Localization accuracy was also measured for a single broadband filtered click and compared to a similar broadband filtered, short-duration noise. Sound Source Localization accuracy was determined as a function of sinusoidal amplitude modulation and the “transposed” process of modulation of filtered noises and a 4 kHz tone. Different rates (16 to 512 Hz) of modulation (including unmodulated conditions) were used. Providing modulation for filtered click stimuli, filtered noises, and the 4 kHz tone had, at most, a very small effect on sound Source Localization accuracy. These data suggest that ampl...
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Sound Source Localization: 1850s to 1950s
Journal of the Acoustical Society of America, 2017Co-Authors: William A. YostAbstract:While scientists and philosophers have been interested in sound Source Localization since the time of the ancient Greeks, the modern study of this topic probably began in the late 19th century. Because sound has no spatial dimensions, there were many arguments at this time as to how humans localize a Source based on the sound it produces. Lord Rayleigh conducted a “garden experiment” and concluded that a binaural ratio of sound level at each ear could account for his ability to identify the location of people who spoke in the garden. This type of experiment began the modern investigation of the acoustic cues used for sound Source Localization. In the first half of the 20th century, psychoacousticians such as Licklider, Jeffress, Mills, Newman, Rosenzweig, Stevens, von Hornbostel, Wallach, Wertheimer, and many others (documented by Boring in Sensation and Perception, 1942 and by Blauert in Spatial Hearing, 1997) added seminal papers leading to our current understanding of sound Source Localization. This pr...
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Sound Source Localization identification accuracy: Level and duration dependencies.
Journal of the Acoustical Society of America, 2016Co-Authors: William A. YostAbstract:Sound Source Localization accuracy for noises was measured for Sources in the front azimuthal open field mainly as a function of overall noise level and duration. An identification procedure was used in which listeners identify which loudspeakers presented a sound. Noises were filtered and differed in bandwidth and center frequency. Sound Source Localization accuracy depended on the bandwidth of the stimuli, and for the narrow bandwidths, accuracy depended on the filter's center frequency. Sound Source Localization accuracy did not depend on overall level or duration.
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Sound Source Localization: Clicks and click trains
Journal of the Acoustical Society of America, 2014Co-Authors: William A. Yost, Xuan Zhong, Anbar NajamAbstract:Tino Trahiotis and Les Bernstein have provided valuable information about human listeners' ability to process interaural time differences in the envelopes of high-frequency carrier signals. These data have enriched models of binaural processing. In this paper, we explore sound Source Localization of click (100 microsecond transients) stimuli in the azimuth plane in the free field. We are especially interested in the sound Source Localization of click trains as they provide stimuli with robust envelope properties. We measured sound Source Localization accuracy for tones, single clicks, and click trains–unfiltered and filtered in low- and high-frequency regions. The filtering was performed to implicate the role of interaural time and level differences in sound Source Localization. These data involving clicks will be compared to recent findings from our laboratory involving sound Source Localization of broadband and filtered noise bursts as compared to sound Source Localization of umodulated and amplitude mo...
Gary W Elko - One of the best experts on this subject based on the ideXlab platform.
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passive acoustic Source Localization for video camera steering
International Conference on Acoustics Speech and Signal Processing, 2000Co-Authors: Yiteng Huang, Jacob Benesty, Gary W ElkoAbstract: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.
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ICASSP - Passive acoustic Source Localization for video camera steering
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000Co-Authors: Yiteng Huang, Jacob Benesty, Gary W ElkoAbstract: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.
Soura Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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Source Localization in wireless sensor networks from signal time of arrival measurements
IEEE Transactions on Signal Processing, 2011Co-Authors: Enyang Xu, Zhi Ding, Soura DasguptaAbstract:Recent advances in wireless sensor networks have led to renewed interests in the problem of Source Localization. Source Localization has broad range of applications such as emergency rescue, asset inventory, and reSource management. Among various measurement models, one important and practical Source signal measurement is the received signal time of arrival (TOA) at a group of collaborative wireless sensors. Without time-stamp at the transmitter, in traditional approaches, these received TOA measurements are subtracted pairwise to form time-difference of arrival (TDOA) data for Source Localization, thereby leading to a 3-dB loss in signal-to-noise ratio (SNR). We take a different approach by directly applying the original measurement model without the subtraction preprocessing. We present two new methods that utilize semidefinite programming (SDP) relaxation for direct Source Localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of Source Localization based on the direct TOA data over time-difference preprocessing.
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ICASSP - Wireless Source Localization based on time of arrival measurement
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Enyang Xu, Zhi Ding, Soura DasguptaAbstract:Wireless Source Localization has found a number of applications in wireless sensor networks. In this work, we investigate Source Localization based on the practical time of arrival (TOA) measurement model. Unlike most existing works that transform TOA measurement into time differences before processing, we consider the original measurement model and investigate three methods for direct Source Localization. We also derive the Cramer-Rao lower bound (CRLB) under the TOA model and establish its connection with the CRLB under the more commonly used time-difference of arrival (TDOA) signal model. We present results that illustrate the performance advantage of Source Localization based on the original TOA model over the commonly used TDOA pre-processing.
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A Semidefinite Programming Approach to Source Localization in Wireless Sensor Networks
IEEE Signal Processing Letters, 2008Co-Authors: Chen Meng, Zhi Ding, Soura DasguptaAbstract:We propose a novel approach to the Source Localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different Source Localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different Source Localization models. Our algorithm can either be used to estimate the Source location or be used to initialize the original nonconvex maximum likelihood algorithm.
J. H. Li - One of the best experts on this subject based on the ideXlab platform.
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Energy-Based Sound Source Localization with Low Power Consumption in Wireless Sensor Networks
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017Co-Authors: F. Deng, S P Guan, X H Yue, X D Gu, J Y Lv, Jin Chen, J. H. LiAbstract:In this study, we show that the energy-based method of sound Source Localization can be successfully exploited for sound Source Localization under low power consumption conditions. Sound Source Localization is widely applied in battlefield environments where low power consumption is especially crucial and necessary for extending the lifespan of sensor nodes. We propose several variables that may (possibly) affect the path loss exponent. We provide data that shows that energy-based methods of sound Source Localization can accurately determine the appropriate path loss exponent and can improve Localization accuracy. The results of our study also demonstrate that energy-based methods of sound Source Localization can significantly reduce Localization errors by adjusting sensors' weight coefficients when ambient (background) noise exists. Our test results indicate that our method of Source Localization using low power consumption is consistently accurate and is able to determine sound Source Localization multiple times over an extended period.
Yiteng Huang - One of the best experts on this subject based on the ideXlab platform.
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Time Delay Estimation and Source Localization
Springer Handbook of Speech Processing, 2020Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong ChenAbstract:A fundamental requirement of microphone arrays is the capability of instantaneously locating and continuously tracking a speech sound Source. The problem is challenging in practice due to the fact that speech is a nonstationary random process with a wideband spectrum, and because of the simultaneous presence of noise, room reverberation, and other interfering speech Sources. This Chapter presents an overview of the research and development on this technology in the last three decades. Focusing on a two-stage framework for speech Source Localization, we survey and analyze the state-of-the-art time delay estimation (TDE) and Source Localization algorithms.
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passive acoustic Source Localization for video camera steering
International Conference on Acoustics Speech and Signal Processing, 2000Co-Authors: Yiteng Huang, Jacob Benesty, Gary W ElkoAbstract: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.
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ICASSP - Passive acoustic Source Localization for video camera steering
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000Co-Authors: Yiteng Huang, Jacob Benesty, Gary W ElkoAbstract: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.
