260 research outputs found

    Exploiting a geometrically sampled grid in the steered response power algorithm for localization improvement

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    The steered response power phase transform (SRP-PHAT) is a beamformer method very attractive in acoustic localization applications due to its robustness in reverberant environments. This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution. A SRP-PHAT localization algorithm based on the GSG method is also introduced. The proposed method exploits the intersections of the discrete hyperboloids representing the TDOA information domain of the sensor array, and projects the whole TDOA information on the space search grid. The GSG method thus allows one to design the sampled spatial grid which represents the best search grid for a given sensor array, it allows one to perform a sensitivity analysis of the array and to characterize its spatial localization accuracy, and it may assist the system designer in the reconfiguration of the array. Experimental results using both simulated data and real recordings show that the localization accuracy is substantially improved both for high and for low spatial resolution, and that it is closely related to the proposed power response sensitivity measure

    Sensory Communication

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    Contains table of contents for Section 2, an introduction, reports on ten research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 5 R01 DC00270National Institutes of Health Grant 5 P01 DC00361National Institutes of Health Grant 2 R01 DC00100National Institutes of Health Grant 7 R29 DC00428National Institutes of Health Grant 2 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-0200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC00625U.S. Navy - Office of Naval Research Grant N00014-91-J-145

    Sensory Communication

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    Contains table of contents on Section 2, an introduction, reports on eleven research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 5 R01 DC00270National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Grant 5 R01 DC00100National Institutes of Health Contract 7 R29 DC00428National Institutes of Health Grant 2 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-0200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC00625U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-181

    Deep learning assisted sound source localization from a flying drone

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    Localization Using Different Amplitude-Panning Methods in the Frontal Horizontal Plane

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    Amplitude panning is the simplest method to create phantom sources in the horizontal plane. The most commonly employed amplitude panning methods are Vector-Base Amplitude Panning (VBAP), Multiple-Direction Amplitude Panning (MDAP), and Ambisonics. This article investigates the localization of frontal phantom sources created by VBAP, MDAP, and Ambisonics (with and without max-rE weighting) at the central listening position in a listening experiment. The experiment was conducted under typical non-anechoic studio listening conditions and utilized pink noise and a regular array of 8 loudspeakers for all methods. The experimental results are compared to different predictors: a binaural localization model using measured binaural room impulse responses, the direction of the measured sound intensity vector, and the directions of the simpler velocity and energy vectors. The article hereby addresses the questions of how close the actually localized directions of the different panning methods are compared to the desired directions, and how good the predictors match the experimental results

    Sound field planarity characterized by superdirective beamforming

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    The ability to replicate a plane wave represents an essential element of spatial sound field reproduction. In sound field synthesis, the desired field is often formulated as a plane wave and the error minimized; for other sound field control methods, the energy density or energy ratio is maximized. In all cases and further to the reproduction error, it is informative to characterize how planar the resultant sound field is. This paper presents a method for quantifying a region's acoustic planarity by superdirective beamforming with an array of microphones, which analyzes the azimuthal distribution of impinging waves and hence derives the planarity. Estimates are obtained for a variety of simulated sound field types, tested with respect to array orientation, wavenumber, and number of microphones. A range of microphone configurations is examined. Results are compared with delay-and-sum beamforming, which is equivalent to spatial Fourier decomposition. The superdirective beamformer provides better characterization of sound fields, and is effective with a moderate number of omni-directional microphones over a broad frequency range. Practical investigation of planarity estimation in real sound fields is needed to demonstrate its validity as a physical sound field evaluation measure. © 2013 Acoustical Society of America

    Physiology-based model of multi-source auditory processing

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    Our auditory systems are evolved to process a myriad of acoustic environments. In complex listening scenarios, we can tune our attention to one sound source (e.g., a conversation partner), while monitoring the entire acoustic space for cues we might be interested in (e.g., our names being called, or the fire alarm going off). While normal hearing listeners handle complex listening scenarios remarkably well, hearing-impaired listeners experience difficulty even when wearing hearing-assist devices. This thesis presents both theoretical work in understanding the neural mechanisms behind this process, as well as the application of neural models to segregate mixed sources and potentially help the hearing impaired population. On the theoretical side, auditory spatial processing has been studied primarily up to the midbrain region, and studies have shown how individual neurons can localize sounds using spatial cues. Yet, how higher brain regions such as the cortex use this information to process multiple sounds in competition is not clear. This thesis demonstrates a physiology-based spiking neural network model, which provides a mechanism illustrating how the auditory cortex may organize up-stream spatial information when there are multiple competing sound sources in space. Based on this model, an engineering solution to help hearing-impaired listeners segregate mixed auditory inputs is proposed. Using the neural model to perform sound-segregation in the neural domain, the neural outputs (representing the source of interest) are reconstructed back to the acoustic domain using a novel stimulus reconstruction method.2017-09-22T00:00:00

    Exploiting CNNs for Improving Acoustic Source Localization in Noisy and Reverberant Conditions

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    This paper discusses the application of convolutional neural networks (CNNs) to minimum variance distortionless response localization schemes. We investigate the direction of arrival estimation problems in noisy and reverberant conditions using a uniform linear array (ULA). CNNs are used to process the multichannel data from the ULA and to improve the data fusion scheme, which is performed in the steered response power computation. CNNs improve the incoherent frequency fusion of the narrowband response power by weighting the components, reducing the deleterious effects of those components affected by artifacts due to noise and reverberation. The use of CNNs avoids the necessity of previously encoding the multichannel data into selected acoustic cues with the advantage to exploit its ability in recognizing geometrical pattern similarity. Experiments with both simulated and real acoustic data demonstrate the superior localization performance of the proposed SRP beamformer with respect to other state-of-the-art techniques
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