One of the key issues of noisy speech enhancement technique is to achieve appropriate statistical distributions to model the clean speech and noise signals accurately. Most of the existing algorithms try to employ a s...
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One of the key issues of noisy speech enhancement technique is to achieve appropriate statistical distributions to model the clean speech and noise signals accurately. Most of the existing algorithms try to employ a sole model assumption in transform domain, which, however, has been proven to being contrary with the fact. To address this problem, the statistical properties of clean speech as well as several noise signals are analyzed using actual data in Discrete cosine transform(DCT) domain, and the study indicates the statistic of clean speech DCT coefficients tending to fall somewhere in between the Gaussian and Laplacian distribution. Based on the results,a novel speech enhancement algorithm is proposed using Gaussian-Laplacian combination model, whose core is employing a linear combination of Gaussian and Laplacian distribution to model the statistic of clean speech DCT coefficients. The corresponding weights of either distribution to the combination model are adaptively adjusted in terms of the probability of each hypothesis, which is estimated based on a soft decision technique by using Bayesian theorem. Through a number of objective and subjective tests,we compare the performance of the proposed algorithm with other recent model based approaches and have found that our algorithm is superior to the related approaches at all testing environments.
The estimation of noise Power spectral density(PSD) is a very crucial issue for speech enhancement as a result of its significant effect on the quality and intelligibility of the enhanced speech. Most of the existing ...
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The estimation of noise Power spectral density(PSD) is a very crucial issue for speech enhancement as a result of its significant effect on the quality and intelligibility of the enhanced speech. Most of the existing estimators for noise PSD try to employ Gaussian speech priors, which, however, have been proven inconsistent with the reality. We derived an effective solution to this problem of estimating noise PSD in the Minimum mean square error(MMSE) sense when the speech component is modeled by a Laplacian distribution. Meanwhile, the soft decision technique instead of the hard Voice activity detection(VAD) is evolved into our algorithm, which can automatically makes the estimation unbiased without requiring a bias compensation. The performance of the proposed method is tested by several objective and subjective measures under various stationary and nonstationary noise environments. The results confirm that our method achieves good performance for all the noise conditions and Signalnoise-ratio(SNR) settings.
Autism spectrum disorder (ASD) presents significant challenges in early detection due to its heterogeneous nature and the subtlety of neurophysiological variations. Electroencephalography (EEG) has emerged as a promis...
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This study investigates the network-based fixed-time stabilization problem of the direct drive wheel (DDW) system of electric vehicles under stochastic cyber-attacks and intermittent denial of service (DoS) attacks vi...
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The problem of spam short message (SMS) recognition involves many aspects of natural language pro- cessing. A good solution to solving the problem can not only improve the quality of people experiencing the mobile l...
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The problem of spam short message (SMS) recognition involves many aspects of natural language pro- cessing. A good solution to solving the problem can not only improve the quality of people experiencing the mobile life, but also has a positive role on promoting the analysis of short text occurring in current mobile applications, such as We- bchat and microblog. As spam SMSes have characteristics of sparsity, transformation and real-timedness, we propose three methods at different levels, i.e., recognition based on sym- bolic features, recognition based on text similarity, and recog- nition based on pattern matching. By combining these meth- ods, we obtain a multi-level approach to spam SMS recog- nition. In order to enrich the pattern base to reduce manual labor and time, we propose a quasi-pattern learning method, which utilizes quasi-pattern matching results in the pattern matching process. The method can learn many interesting and new patterns from the SMS corpus. Finally, a comprehensive analysis indicates that our spare SMS recognition approach achieves a precision rate as high as 95.18%, and a recall rate of 95.51%.
With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so *** this makes people’s lives more convenient,it also increases the risk of the network b...
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With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so *** this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious ***,it is important to identify malicious codes on computer systems ***,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited ***,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution *** feature slicing module reduces the number of parameters by grouping *** multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel *** addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model *** malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,*** proves that LCMISNet has a powerful malicious code recognition *** addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.
Purpose: As main mode of modern service industry and future economy society, the research on crowd network can greatly facilitate governances of economy society and make it more efficient, humane, sustainable and at t...
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Purpose: As main mode of modern service industry and future economy society, the research on crowd network can greatly facilitate governances of economy society and make it more efficient, humane, sustainable and at the same time avoid disorders. However, because most results cannot be observed in real world, the research of crowd network cannot follow a traditional way. Simulation is the main means to put forward related research studies. Compared with other large-scale interactive simulations, simulation for crowd network has challenges of dynamic, diversification and massive participants. Fortunately, known as the most famous and widely accepted standard, high level architecture (HLA) has been widely used in large-scale simulations. But when it comes to crowd network, HLA has shortcomings like fixed federation, limited scale and agreement outside the software system. Design/methodology/approach: This paper proposes a novel reflective memory-based framework for crowd network simulations. The proposed framework adopts a two-level federation-based architecture, which separates simulation-related environments into physical and logical aspect to enhance the flexibility of simulations. Simulation definition is introduced in this architecture to resolve the problem of outside agreements and share resources pool (constructed by reflective memory) is used to address the systemic emergence and scale problem. Findings: With reference to HLA, this paper proposes a novel reflective memory-based framework toward crowd network simulations. The proposed framework adopts a two-level federation-based architecture, system-level simulation (system federation) and application-level simulation (application federations), which separates simulation-related environments into physical and logical aspect to enhance the flexibility of simulations. Simulation definition is introduced in this architecture to resolve the problem of outside agreements and share resources pool (constructed by re
Purpose: Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good ...
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Purpose: With the development of the modern economy, vehicles are no longer a luxury for people, which greatly facilitate people’s daily life, but at the same time bring traffic congestion. How to relieve traffic con...
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As number of bloggers grows rapidly, the influence of microblog is increasing. The spread of rumor on the microblog network affects people's real life. It is of vital practical significance to study and predict ru...
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