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- W2800149766 abstract "This work proposes a Deep neural network (DNN) based method for reconstructing speech magnitude spectrum from Mel-frequency cepstral coefficients (MFCCs). We train a DNN using MFCC vectors as input and the corresponding speech magnitude spectrum as desired output. Exploiting the strong inference power of DNN, the proposed method has the capability to accurately estimate the speech magnitude spectrum even from truncated MFCC vectors. Experiments on TIMIT corpus demonstrate that the proposed method achieves significantly better performance compared with traditional methods." @default.
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- W2800149766 date "2018-03-01" @default.
- W2800149766 modified "2023-10-14" @default.
- W2800149766 title "Speech Magnitude Spectrum Reconstruction from MFCCs Using Deep Neural Network" @default.
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- W2800149766 doi "https://doi.org/10.1049/cje.2017.09.018" @default.
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