2018
DOI: 10.30819/4759
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Modeling of Moving Sound Sources Based on Array Measurements

Abstract: When auralizing moving sound sources in Virtual Reality (VR) environments, the two main input parameters are the location and radiated signal of the source. An array measurement-based model is developed to characterize moving sound sources regarding the two parameters in this thesis. This model utilizes beamforming, i.e. delay and sum beamforming (DSB) and compressive beamforming (CB) to obtain the locations and signals of moving sound sources. A spiral and a pseudorandom microphone array are designed for DSB … Show more

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Cited by 2 publications
(2 citation statements)
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“…Compared with the LS method, LASSO has additionally a penalty term ξ i 1 , i.e., the l 1 norm of the optimized vector. The l 1 norm not only brings sparsity but also keeps the objective function convex, so that the l 1 norm demands less computation time than the non-convex l 0 problem [41]. Following the standard of Matlab [42], the factor 1 2n y adjusts the scale of l 2 norm the residuum and the hyperparameter λ balances the weighting of l 1 and l 2 norms.…”
Section: Methodsmentioning
confidence: 99%
“…Compared with the LS method, LASSO has additionally a penalty term ξ i 1 , i.e., the l 1 norm of the optimized vector. The l 1 norm not only brings sparsity but also keeps the objective function convex, so that the l 1 norm demands less computation time than the non-convex l 0 problem [41]. Following the standard of Matlab [42], the factor 1 2n y adjusts the scale of l 2 norm the residuum and the hyperparameter λ balances the weighting of l 1 and l 2 norms.…”
Section: Methodsmentioning
confidence: 99%
“…A large variety of dry signals are available in databases [77]. On the other hand, signals from the moving sound sources can be measured and classified in terms of sound characteristics and then synthesized [73].…”
Section: Source Signalsmentioning
confidence: 99%