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Fig. 8 Log error of diffuse PSD estimators.
Therefore, it is crucial for blocking-based noise PSD estimators to exhibit small speech leakage.
It is furthermore instructive to investigate the performance of the proposed noise PSD estimators in the presence of nonstationary noise.
Moreover, for the sake of completeness, the studied speech and noise-subspace noise PSD estimators are compared to other binaural and single-channel noise PSD estimators available in the literature: the improved CPSD method (ImCPSD) [22] and the single-channel SPP-based method (SC-SPP) [63].
The noise PSD estimators rely on adaptive target speech cancelation.
Moreover, a class of binaural noise PSD estimators based on speech blocking has been discussed.
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However, when the sample size was set at 40, the PS-matching estimators outperformed better than the IPTW estimators.
When N was set at 40, the PS matching estimators were either similarly or even less biased than the IPTW estimators.
However, when N was set at 40, the PS-matching estimators were either similarly or even less biased than the IPTW estimators.
The constant population model performed worst for both PS and SS estimators (Table 3).
The best model according to Bayes factors using the PS and SS estimators was logistic growth (with a difference of more than four units of –lnLH), followed by exponential growth (Table 3).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com