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Deep neural network-based linear predictive parameter estimations for speech enhancement
Li, Yaxing,Kang, Sangwon IET 2017 IET signal processing Vol.11 No.4
<P>This study presents a speech enhancement technique to improve noise corrupted speech via deep neural network (DNN)-based linear predictive (LP) parameter estimations of speech and noise. With regard to the LP coefficient estimation, an enhanced estimation method using a DNN with multiple layers was proposed. Excitation variances were then estimated via a maximum-likelihood scheme using observed noisy speech and estimated LP coefficients. A time-smoothed Wiener filter was further introduced to improve the enhanced speech quality. Performance was evaluated via log spectral distance, a composite multivariate adaptive regression splines modelling-based measure, and a segmental signal-to-noise ratio. The experimental results revealed that the proposed scheme outperformed competing methods.</P>
Li, Yaxing,Kang, Sangwon IET 2016 IET signal processing Vol.10 No.4
<P>The authors propose a robust artificial bandwidth extension (ABE) technique to improve narrowband (NB) speech signal quality using an enhanced spectrum envelope and excitation estimation. For envelope estimation, they propose an enhanced envelope estimation method using a deep neural network with multiple layers. For excitation estimation, they use a whitened NB excitation signal that is generated by passing the excitation signal through a whitening filter. An adaptive spectral double shifting method is introduced to obtain an enhanced wideband (WB) excitation signal. The proposed ABE system is applied to the decoded output of an adaptive multi-rate (AMR) codec at 12.2 kbps. They evaluate its performance using log spectral distortion, a WB perceptual evaluation of speech quality, and a formal listening test. The objective and subjective evaluations confirm that the proposed ABE system provides better speech quality than AMR at the same bit rate.</P>
Bayesian change point analysis for extreme daily precipitation
Chen, Si,Li, Yaxing,Kim, Jinheum,Kim, Seong W. John Wiley Sons, Ltd 2017 International journal of climatology Vol.37 No.7
<P><B>ABSTRACT</B></P><P>Change point (CP) analysis of extreme precipitation plays a key role to incorporate non‐stationarity in flood predictions under climate change. This article provides a Bayesian method to detect the CP frequently appearing in extreme precipitation data. Unlike most published work based on a normal distribution, we allow for the model to follow a generalized Pareto distribution to fit extreme precipitation over a high threshold with a CP, which can effectively utilize tail behaviour of the distribution. The Bayesian CP detection is investigated on four models: a no change model, a shape change model, a scale change model, and both a shape and scale change model. Model selection is performed using the Bayes factor and model posterior probability; the posterior means of the unknown CP and the model parameters before and after the CP can be obtained based on the selected CP model. Simulation studies and a real data example are provided to demonstrate the proposed methodologies. Finally, model uncertainty issues in the frequency analysis are extensively discussed. It is found that considering the abrupt and sustained CP in extreme precipitation is important when performing hydraulic or hydrologic design.</P>
Zhang Junqin,Li Yaxing,Ren Yanan,Han Hua,Li Jie 한국유전학회 2022 Genes & Genomics Vol.44 No.8
Background: Radiotherapy resistance affects the therapeutic effect of cervical squamous cell carcinoma (CSCC). Smoothened (Smo) is an anticancer target of the Hedgehog (Hh) pathway and its mutation is related to drug resistance. Objective: To explore the roles of miR-326 and Smoothened (SMO) on radiation resistance in patients with cervical carcinoma. Methods: Expression of miR-326 and SMO in cervical cancer tissue and radioresistant cell lines were analyzed. The radiation response with the expression of miR-326 was evaluated in tissue and cells. Bioinformatics analysis and literature review were performed to explore the target of miR-326. The regulation of miR-326 to SMO mRNA was verified through the dual-luciferase reporter assay. Results: Patients with poor radiation response have lower miR-326 and higher SMO expression. Upregulation of miR-326 decreased SMO expression and its downstream proteins but does not affect the proliferation of CSCC cells. The upregulation of miR-326 increased radiation sensitivity of the CSCC cell through downregulating SMO and its downstream proteins in the Hedgehog (Hh) signaling pathway. Conclusions: miR-326 may predict the treatment response to radiation, and upregulating miR-326 may improve the treatment response to radiation.
( Jianchao Bian ),( Shoushan Luo ),( Wei Li ),( Yaxing Zha ),( Yixian Yang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.10
Traditional regenerating codes are designed to tolerate node failures with optimal bandwidth overhead. However, there are many types of partial failures inside the node, such as latent sector failures. Recently, proposed regenerating codes can also repair intra-node failures with node-level redundancy but incur significant bandwidth and I/O overhead. In this paper, we construct a new scheme of regenerating codes, called IR-RBT codes, which employs intra-node redundancy to tolerate intra-node failures and serve as the help data for other nodes during the repair operation. We propose 2 algorithms for assigning the intra-node redundancy and RBT-Helpers according to the failure probability of each node, which can flexibly adjust the helping relationship between nodes to address changes in the actual situation. We demonstrate that the IR-RBT codes improve the bandwidth and I/O efficiency during intra-node failure repair over traditional regenerating codes but sacrifice the storage efficiency.