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In this section we discuss each one of these by providing their definitions, key implementation parameters, and how they are incorporated in the simulation.
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Key implementation issues are also discussed.
Table 3 describes the detailed implementation parameters.
We perceive the key implementation principles to be as follows.
Fig. 10 Frequency of key implementation by category.
We have analyzed the influence of the implementation parameters of our SE front-end, which revealed that using dereverberation prior to denoising and linear filtering to reduce reverberation were keys to achieving high recognition performance.
Full knowledge of the neural key implementation is required to land a successful extraction of keys.
Key implementation decisions were also made by the project.
A discussion of key implementation and research gaps follows.
Fig. 9 Frequency of keys implementation (N = 48 companies).
Gibbs sampling was used to sample parameters from their full conditional distributions, which are derived for some key model parameters in Appendix C. Since the implementation of a Gibbs sampler in the additive model has been well described by [ 6], here we focus on the algorithm for the dominance model.
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