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• W3005655103 abstract "Phase reconstruction, which estimates phase from a given amplitude spectrogram, is an active research field in acoustical signal processing with many applications including audio synthesis. To take advantage of rich knowledge from data, several studies presented deep neural network (DNN)--based phase reconstruction methods. However, the training of a DNN for phase reconstruction is not an easy task because phase is sensitive to the shift of a waveform. To overcome this problem, we propose a DNN-based two-stage phase reconstruction method. In the proposed method, DNNs estimate phase derivatives instead of phase itself, which allows us to avoid the sensitivity problem. Then, phase is recursively estimated based on the estimated derivatives, which is named recurrent phase unwrapping (RPU). The experimental results confirm that the proposed method outperformed the direct phase estimation by a DNN." @default.
• W3005655103 created "2020-02-24" @default.
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• W3005655103 date "2020-02-13" @default.
• W3005655103 modified "2023-09-26" @default.
• W3005655103 title "Phase reconstruction based on recurrent phase unwrapping with deep neural networks" @default.
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• W3005655103 doi "https://doi.org/10.48550/arxiv.2002.05832" @default.
• W3005655103 hasPublicationYear "2020" @default.
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