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A gunshot detection method under noisy environments was explained in [6].
Isolated word recognition experiments demonstrated the effectiveness of the proposed method under noisy conditions.
The sensitivity of the proposed method under noisy conditions has been investigated.
Specifically, the relations of the approach of Prony to an annihilating filter in rate of innovation and ELP in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method under noisy environments.
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The result of the numerical simulation shows the advantage of the proposed method under a noisy situation.
In addition we show how nonlinear ℓ1 methods for finding sparse models can be competitive in speed with the widely used ℓ2 methods, certainly under noisy conditions, so that there is no need to shun ℓ1 penalizations.
In essence, this method implements a test for potentials approaching each other closely in time, yet with the advantage that this method also works under noisy conditions.
The use of numerical differentiation procedures is identified as the main cause for the poor performance of the modal curvature method under sparse and noisy measurement.
Hyvärinen introduced the so-called "sparse code shrinkage" method, which estimates non-Gaussian data under noisy conditions and is based on the maximum likelihood estimation principle [20].
Future work will focus on a method to obtain RIR in real time under noisy reverberant environments to realize real-time and steady improvement of speech intelligibility in variable room boundary conditions.
The performance of the proposed method has been compared to some of the existing methods for estimating the frequency and damping factor of a signal under noisy conditions, such as the Prony and EKF methods.
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