Галерея 3025638
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Галерея 3025638
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Site-directed mutagenesis was used to change Lys-128 of the simian virus 40 large-T nuclear location signal to Met, Ile, Arg, Gln, Asn, Leu, or His. Except for the large-T antigen of the Arg mutation, which was present in cytoplasmic and nuclear compartments, the resultant proteins were unable to enter the nucleus. By contrast, mutations at other sites within the signal were generally less severe in their effect. In some cases (Lys-128 to Gln, Asn, and His), the apparently cytoplasmic variants were able to support limited plasmid DNA replication, suggesting that low levels of large-T antigen undetectable by immunofluorescence were present in the nucleus. Such mutants did not support viral DNA replication. We conclude that there is a strong requirement for a basic residue at position 128 in the large-T nuclear location signal, with Lys the preferred residue.
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Abstract: Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to m... View more
Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model generalizability to noisy conditions: (1) mismatched noisy condition during testing, i.e., the performance is generally sub-optimal when models are tested with unseen noise types that are not involved in the training data; (2) local focus on specific noisy conditions, i.e., models trained using multiple types of noises cannot optimally remove a specific noise type even though the noise type has been involved in the training data. These problems are common in real applications. In this article, we propose a novel denoising autoencoder with a multi-branched encoder (termed DAEME) model to deal with these two problems. In the DAEME model, two stages are involved: training and testing. In the training stage, we build multiple component models to form a multi-branched encoder based on a decision tree (DSDT). The DSDT is built based on prior knowledge of speech and noisy conditions (the speaker, environment, and signal factors are considered in this paper), where each component of the multi-branched encoder performs a particular mapping from noisy to clean speech along the branch in the DSDT. Finally, a decoder is trained on top of the multi-branched encoder. In the testing stage, noisy speech is first processed by each component model. The multiple outputs from these models are then integrated into the decoder to determine the final enhanced speech. Experimental results show that DAEME is superior to several baseline models in terms of objective evaluation metrics, automatic speech recognition results, and quality in subjective human listening tests.
Date of Publication: 05 October 2020
TABLE I PESQ Scores of the DAEME-UAT {_6} System at Four Noise Types and Six SNRs. Avg. Denotes the Average Scores
TABLE II STOI Scores of the DAEME-UAT {_6} System at Four Noise Types and Six SNRs. Avg. Denotes the Average Scores
TABLE III PESQ Scores of the DAEME-USAT _{(SS)(12)} and DAEME-USAT _{(WD)(12)} Systems at Four Noise Types and Six SNRs. Avg. Denotes the Average Scores
TABLE IV STOI scores of the DAEME-USAT _{(SS)(12)} and DAEME-USAT _{(WD)(12)} Systems At Four Noise Types and Six SNRs. Avg. Denotes The Average Scores
TABLE V PESQ Scores of Seen Data of BLSTM and DAEME
TABLE VI STOI Scores of Seen Data of BLSTM and DAEME
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