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- W1544785557 abstract "This paper investigates a multi-channel denoising autoencoder (DAE)-based speech enhancement approach. In recent years, deep neural network (DNN)-based monaural speech enhancement and robust automatic speech recognition (ASR) approaches have attracted much attention due to their high performance. Although multi-channel speech enhancement usually outperforms single channel approaches, there has been little research on the use of multi-channel processing in the context of DAE. In this paper, we explore the use of several multi-channel features as DAE input to confirm whether multi-channel information can improve performance. Experimental results show that certain multi-channel features outperform both a monaural DAE and a conventional time-frequency-mask-based speech enhancement method." @default.
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- W1544785557 date "2015-04-01" @default.
- W1544785557 modified "2023-09-26" @default.
- W1544785557 title "Exploring multi-channel features for denoising-autoencoder-based speech enhancement" @default.
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- W1544785557 doi "https://doi.org/10.1109/icassp.2015.7177943" @default.
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