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  • Open Access


    Speech Enhancement via Mask-Mapping Based Residual Dense Network

    Lin Zhou1,*, Xijin Chen1, Chaoyan Wu1, Qiuyue Zhong1, Xu Cheng2, Yibin Tang3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1259-1277, 2023, DOI:10.32604/cmc.2023.027379

    Abstract Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network (DNN). But the mapping-based methods only utilizes the phase of noisy speech, which limits the upper bound of speech enhancement performance. Masking-based methods need to accurately estimate the masking which is still the key problem. Combining the advantages of above two types of methods, this paper proposes the speech enhancement algorithm MM-RDN (masking-mapping residual dense network) based on masking-mapping (MM) and residual dense network (RDN). Using the logarithmic power spectrogram (LPS) of consecutive frames, MM estimates the ideal… More >

  • Open Access


    Speech Enhancement via Residual Dense Generative Adversarial Network

    Lin Zhou1,*, Qiuyue Zhong1, Tianyi Wang1, Siyuan Lu1, Hongmei Hu2

    Computer Systems Science and Engineering, Vol.38, No.3, pp. 279-289, 2021, DOI:10.32604/csse.2021.016524

    Abstract Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which… More >

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