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중첩 U-Net 기반 음성 향상을 위한 다중 레벨 Skip Connection
황서림(Seorim Hwang),변준(Joon Byun),허준영(Junyeong Heo),차재빈(Jaebin Cha),박영철(Youngcheol Park) 한국방송·미디어공학회 2022 방송공학회논문지 Vol.27 No.6
In a deep neural network (DNN)-based speech enhancement, using global and local input speech information is closely related to model performance. Recently, a nested U-Net structure that utilizes global and local input data information using multi-scale has been proposed. This nested U-Net was also applied to speech enhancement and showed outstanding performance. However, a single skip connection used in nested U-Nets must be modified for the nested structure. In this paper, we propose a multi-level skip connection (MLS) to optimize the performance of the nested U-Net-based speech enhancement algorithm. As a result, the proposed MLS showed excellent performance improvement in various objective evaluation metrics compared to the standard skip connection, which means that the MLS can optimize the performance of the nested U-Net-based speech enhancement algorithm. In addition, the final proposed model showed superior performance compared to other DNN-based speech enhancement models.