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Impairment Noise-corrupted Noise-suppressed PESQ P.563 E-model PESQ P.563 E-model Codec-PLC — Packet loss rate — — NI NI NI Noise type — Noise level —.
We consider a stochastic Korteweg de Vries equation forced by a random term of white noise type.
The existing subspace clustering models mainly adopt a deterministic loss function to describe a certain noise type between an observed data matrix and its self-expressed form.
Wang et al. (2014) developed a data mining approach for noise type diagnosis and proposed a fuzzy filter design for enhancing the quality of noise corrupted images.
This paper develops a data mining approach for noise type diagnosis, and proposes a fuzzy filter design for enhancing the quality of noise corrupted images.
Furthermore, we investigated the individual performance on each noise type.
Table 7 WER of ESniff MMSE (M = 4) method for Aurora 2 task using FE feature vectors SNR (dB) Set A Set B Set C Noise type Noise type Noise type Sub.
Since noise type mismatch can significantly impact performance, a strategy to address any noise type difference between training and test noisy speech should be employed.
The average performance for each noise type and trial condition is shown in Table2.
We consider a single noise codebook, C w, corresponding to the encountered noise type.
For each noise type and SNR value, there are 1,001 sentences for recognition.
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