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Figure 13 Error comparison between circle-based method and proposed method in noisy situation (salt and pepper noise).
The proposed algorithm is fairly compared with the optimal GCC methods, a generalized linear regression estimator, and an AMDF method in noisy and reverberant environment.
In contrast with [19], the experiments used in this paper include the use of real robots and make it possible to verify the robustness of the method in noisy environments.
We refer to this method as affine NMF, and the effectiveness of this method has been confirmed by comparing its effectiveness with that of a conventional NMF-based method and a GMM-based method in noisy environments.
It can be realized economically using wavelets with shortest length, such as Harr, Db2, Sym2 or Coif 1. Two types of noise, namely, Gaussian white noise and band limited spectrum noise are considered in this paper to show the effectiveness of the proposed method in noisy environment.
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Figure 6 shows that the distortion of the proposed method is lower than that of the conventional NMF and GMM-based methods in noisy environments.
The performance of the proposed method is compared with that of conventional cross-correlation and linear regression-based methods in noisy and reverberant environment.
Since the results are encouraging, it is worth to look for noise-robust methods of deriving pitch and formants, and then to investigate the proposed methods in noisy conditions.
When employing the AFE feature vectors, an improvement in performance was also observed using the proposed methods in noisy speech recognition, although the relative improvement over conventional methods was somewhat reduced due to the integrated speech enhancement algorithm inherent in the AFE.
Test results confirmed accuracy and robustness of the method even in noisy STED images of gp210.
Thus, we confirm the effectiveness of the proposed method in various noisy environments.
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