Speech Enhancement via Residual Dense Generative Adversarial Network

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 can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

Influence of content and creator characteristics on sharing disaster-related information on social media

Identifying the key factors of the disaster-related information propagation process can provide decision support for disaster management. This study characterizes the effects of content types, location, and social capital of social media users on the virality of disaster-related information. We found through the Weibo dataset of the Yiliang earthquake that the virality of different types of information can vary on the basis of the social capital of users who post the information. This study fills the current research gaps by examining the individual and joint effects of the content and creator characteristics on the virality of disaster-related information.     

Entity-level sentiment prediction in Danmaku video interaction

The Journal of Supercomputing - Sentiment analysis in Danmaku video interaction aims at measuring public mood in respect of the video, which is helpful for the potential applications in behavioral...     

Influential facilities placement over moving objects

Distributed and Parallel Databases - In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate...     

From text to graph: a general transition-based AMR parsing using neural network

Neural Computing and Applications - Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing,...     

Toward practical adoption of i* framework: an automatic two-level layout approach

Requirements Engineering - Bridging the gap between academia and industry is an important issue to promote the practicality of i* framework. Researchers have been dealing with this issue from...     

Image classification method on class imbalance datasets using multi-scale CNN and two-stage transfer learning

Neural Computing and Applications - Image classification tasks widely exist in many actual scenarios, including medicine, security, manufacture and finance. A major problem that hurts algorithm...     

Incorporating Actor-Critic in Monte Carlo tree search for symbolic regression

Neural Computing and Applications - Most traditional genetic programming methods that handle symbolic regression are random algorithms without memory and direction. They repeatedly search for the...     

Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model

Based on the multi-agent model, an artificial stock market with four types of traders is constructed. On this basis, this paper focuses on comparing the effects of liquidation behavior on market liquidity, volatility, price discovery efficiency and long memory of absolute returns when the institutional trader adopts equal-order strategy, Volume Weighted Average Price (VWAP) strategy and Implementation Shortfall (IS) strategy respectively. The results show the following: (1) the artificial stock market based on multi-agent model can reproduce the stylized facts of real stock market well; (2) among these three algorithmic trading strategies, IS strategy causes the longest liquidation time and the lowest liquidation cost; (3) the liquidation behavior of institutional trader will significantly reduce market liquidity, price discovery efficiency and long memory of absolute returns, and increase market volatility; (4) in comparison, IS strategy has the least impact on market liquidity, volatility and price discovery efficiency, while VWAP strategy has the least impact on long memory of absolute returns.

     

无线鼠标电路设计及算法实现

文中设计的无线鼠标包含发射电路和接收电路两部分,发射电路先采用光传感器ADNS5030检测鼠标的移动信息,然后输出到内置单片机的无线模块CC2430的I/O口,通过CC2430控制并将此信息发射到接收电路,接收电路接收到移动信息后,根据鼠标位移状态机解码算法,得到移动信息及按键信息,通过HT82M98A处理后将信息传给计算机,计算机自动完成相应动作;然后不断的循环;经测试,该电路不仅具备普通有线鼠标的全部功能,还可远距离灵活操纵鼠标,在大礼堂等开阔场所可达80 m以上;计算机自动识别无线鼠标接收电路,不需要额外安装程序,每一次鼠标信号处理总时间不大于1 ms,无线鼠标移动灵活,无停滞现象.