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In order to understand the image formation inside plenoptic systems, a wave-optic-based model is proposed in this paper that uses the Fresnel diffraction equation to propagate the whole object field into the plenoptic systems. The proposed model is much flexible at sampling on propagation planes by utilizing the method of multiple partial propagations. In order to verify the effectiveness of the proposed model, numerical simulations are conducted by comparing with existing wave optic model under different optical configurations of plenoptic cameras.

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In order to understand the image formation inside plenoptic systems, a wave-optic-based model is proposed in this paper that uses the Fresnel diffraction equation to propagate the whole object field into the plenoptic systems. The proposed model is much flexible at sampling on propagation planes by utilizing the method of multiple partial propagations. In order to verify the effectiveness of the proposed model, numerical simulations are conducted by comparing with existing wave optic model under different optical configurations of plenoptic cameras.

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8 Views

Ambisonics is a series of flexible spatial sound systems based on spatial harmonics decomposition and each order approximation of sound field. Accuracy and complexity of system increase with order. Considering that the horizontal localization resolution of human hearing is higher than vertical resolution, mixed-order Ambisonics (MOA) reconstructs horizontal sound field with higher order spatial harmonics, while reconstructs vertical sound field with lower order spatial harmonics, and thereby reaches a compromise between the perceptual performance and the complexity of system.

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This paper considers the target localization problem using the hybrid bistatic range and time difference of arrival (TDOA) measurements in multistatic radar. An algebraic closed-form solution to this nonlinear estimation problem is developed through two-stage processing, where the nuisance variables are introduced in the first stage and the localization error of first stage solution is estimated to improve the final target position estimate in the second stage.

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53 Views

Bidirectional long short term memory (BLSTM) recurrent neural networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and deep neural networks (DNNs) in automatic language identification (LID), particularly when testing with very short utterances (∼3s). Mismatches conditions between training and test data, e.g. speaker, channel, duration and environmental noise, are a major source of performance degradation for LID.

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We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to enhance the performance of a deep neural network (DNN) based ASR system. We call the scheme speaker-invariant training (SIT). In SIT, a DNN acoustic model and a speaker classifier network are jointly optimized to minimize the senone (tied triphone state) classification loss, and simultaneously mini-maximize the speaker classification loss.

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16 Views

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