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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Yan Huang HaiLun Lian Jian Zhou HuaBin Wang Liang Tao MOE Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

In this paper, we propose a method of enhancing whisper, using whisper without any pretreatment combined with Wavenet. Our method is end-to-end, that is, inputing noised whisper to get clean whisper. The input to our method is the original whisper without any processing, reducing the loss of features caused by other operations. We use speech denoising Wavenet to enhance whisper. Wavenet can not only enhance whisper well, but also tackle the issue of intelligibility. Specifically, use symmetric dilated convolution to obtain noisy speech context, help the model to enhance the speech for better denoising effect. Experimental results show that the enchanced whisper gains better performance both in the aspect of speech quality and intelligibility.

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Whispered Speech to Normal Speech Conversion Using Bidirectional LSTMs with Meta-network

Whispered Speech to Normal Speech Conversion Using Bidirecti...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： WeiWei Yu HaiLun Lian Jian Zhou HuaBin Wang Liang Tao MOE Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

In this paper, we are interested in the conversion of whispered to normal speech. The baseline method uses standard bidirectional LSTM (BLSTM) RNN to predict both the spectral features and excitation parameters of the normal speech from whispered speech. Also, it employs STRAIGHT speech synthesizer. The BLSTM based whispered speech to normal speech conversion system is among the best systems in term of the naturalness of generated speech. However, in many cases, the model complexity and inference cost of BLSTM prevents its usage. As opposed to using standard BLSTM with sharing values, we propose a meta-network to generate non-shared weights for LSTM memory block in BLSTM (denote as meta-BLSTM). Besides, we use a low-rank approximation to generate the parameter matrix, which can reduce the model complexity. To our knowledge, this is the first study that uses meta-network to train a whispered to normal speech conversion system. To evaluate the performance of the proposed system, we performed experiments in the TIMIT dataset. Experimental results show that the proposed method achieves state-of-the-art performance.

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Posterior Probability Support Vector Machine Applied in Motor Imagery Classification

Posterior Probability Support Vector Machine Applied in Moto...

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International Conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Ying-ying Jiao Xiao-pei Wu Key Laboratory of Intelligent Computing &Signal Processing of MOE Anhui University Hehui China

Abstract-Brain-computer interface (BCI) which transforms signals from the brain into control signals can help people with disabilities communicate with others. In this paper, posteriori probability support vector machine (PPSVM) for patterns recognition was developed. For the classification of the left or right hand motor imagery, this method was used to expend the training set by adding samples with great probability output. For the dataset from 2003 BCI Competition, AR model was adopted to extract feature vectors and SVM with posteriori probabilistic output was used to classify the dataset. The results proved that, by adding samples with big probability, the performance of BCI was improved and higher accuracy was achieved.

关键词： Training Support vector machines Feature extraction Brain modeling Testing Accuracy Kernel

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An Optimization Method Based on Deep Learning for Electromagnetic Inverse Scattering Problems

An Optimization Method Based on Deep Learning for Electromag...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Hu, Wangchao Xie, Guoda Dai, Jinpeng Hou, Guilin Li, Xiang Huang, Zhixiang Anhui University The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Education of Anhui Province Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9798350355581

In this paper, a deep learning optimization method combining U-net model and cycle generative adversarial network (Cycle-GAN) is proposed to efficiently solve the electromagnetic inverse scattering (EMIS) problems. Firstly, a U-net model is used to obtain a rough preliminary reconstruction. Through down-sampling and up-sampling processes, the basic features of the object are extracted and reconstructed from the measured scattered field, providing a coarse dielectric parameter distribution. Then, a Cycle-GAN model further refines the output of the U-net. The Cycle-GAN model, through its adversarial training mechanism involving a generator and a discriminator, enhances the details of the preliminary reconstruction, resulting in a finely detailed dielectric parameter distribution his method improves the problem of rough results obtained by using U-net method alone, and can effectively solve and optimize EMIS problems. © 2024 IEEE.

关键词： Generative adversarial networks

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Application of a multi-factor model in the grain yield prediction

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Metallurgical and Mining Industry 2015年第8期7卷 468-474页

作者： Chen, Lili Guo, Hongjun Laboratory of Intelligent Information Processing Suzhou University Suzhou Anhui China The Key Laboratory of Intelligent Computing and Signal Processing of MOE Anhui University Hefei Anhui China

A multi-factor model for the grain yield prediction based on the previous data and relevant impacting factors is reported. In this model, the weight coefficient of each individual factor that affected the grain yield historically is analyzed by the variation coefficient method and the conversion degree function in the attribute theory. The effects of various factors on the predicted grain yield are then used as standard vectors and subjected to a similarity-based search in the matrix of historical values. The predicted total grain yield is then determined by multiplying the sown area by the unit grain yield which is obtained by the highest similarity between the historical data and those predicted. Compared to the results obtained by the BP nerve network method, this method is simpler, more flexible, less time-consuming, and more accurate.

关键词： Forecasting

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Wave separation for the great Sumatra-Andaman earthquake with regional seismic array

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Earthquake Science 2011年第1期24卷 127-132页

作者： Chunyan Zhang Sidao Ni Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematics Anhui University Hefei 230039 China Key Laboratory of Dynamic Geodesy Institute of Geodesy and Geophysics Chinese Academy of Sciences Wuhan 430077 China

We analyzed the seismic waveforms from the December 26, 2004 Sumatra-Andaman earthquake recorded at broadband seismic stations in western Europe. Previous studies involving of the beam-forming technique and high frequency analysis suggest that the earthquake ruptured with a duration of around 500 s. This very long duration makes P wave overlap with later arrivals such as PP wave, which follows P in about 200 s. Since P waves are crucial for modeling earthquake processes, we propose an iterative method to separate P and PP waveforms. The separated P waveform confirms a second large energy release around 300 s after the initial rupture. The iterative signal separation technique is particularly useful for mixed signals that are not independent and the number of recording stations far exceeds number of mixed signal sources.

关键词： rupture duration great Sumatra-Andaman earthquake wave separation array analysis

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A Circularly Polarized Antenna with a Loading Metal Strip for GPS L1 band

A Circularly Polarized Antenna with a Loading Metal Strip fo...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Meng, Lulu Qian, Sixian Huang, Zhixiang Li, Yingsong Loh, Tian Hong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic & Eletrochemical Technologies Department Middlesex United Kingdom

ISBN: (纸本)9781733509633

The paper presents a novel circular polarization(CP) antenna loading with a parasitic ring metal strip, which is designed for global positioning system (GPS) L1 band applications. The antenna consists of a defected ground plane with four arc-slots and a circular slot, a dielectric substrate, a parasitic ring metal strip, and a radiating patch (RP) with a arc-slot. By rotating the parasitic ring metal strip to opening slot on the RP, the antenna can be transformed from left-handed circular polarization (LHCP) to right-handed circular polarization (RHCP). The size of the antenna can be reduced by using slots on the ground. The simulation and optimization results show that impedance bandwidth of the L/RHCP antenna is over 100 MHz, and the axial ratio bandwidth is over 20 MHz. © 2023 ACES.

关键词： Global positioning system

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Hidden Attractors in a Delayed Memristive Differential System with Fractional Order and Chaos Synchronization

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Journal of Harbin Institute of Technology(New Series) 2020年第1期27卷 67-75页

作者： Dawei Ding Yecui Weng Nian Wang School of Electronics and Information Engineering Anhui UniversityHefei 230601China Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityHefei 230601China

As an important research branch,memristor has attracted a range of scholars to study the property of memristive chaotic ***,time⁃delayed systems are considered a significant and newly⁃developing field in modern *** combining memristor and time⁃delay,a delayed memristive differential system with fractional order is proposed in this paper,which can generate hidden ***,we discussed the dynamics of the proposed system where the parameter was set as the bifurcation parameter,and showed that with the increase of the parameter,the system generated rich chaotic phenomena such as bifurcation,chaos,and *** we derived adequate and appropriate stability criteria to guarantee the system to achieve ***,examples were provided to analyze and confirm the influence of parameter a,fractional order q,and time delayτon chaos *** simulation results confirm that the chaotic synchronization is affected by a,q andτ.

关键词： fractional order memristive time⁃delay hidden attractors chaos synchronization

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The quotient structure in granule computing

The quotient structure in granule computing

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2005 IEEE International Conference on Granular computing

作者： Zhao, Shu Zhang, Yan-Ping Zhang, Ling Chen, Jie Wan, Zhong Zhang, Ying-Chun Zhang, Chen-Xi Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 Institute of Artificial Intelligence Anhui University Hefei 230039

ISBN: (纸本)0780390172

The theory of granule computing based on the quotient space is one of the three main granule computing theories. The emphasis is on the structure of the quotient space theory in this paper. Comparing with Rough Set theory, the authors point out the importance of the structure in granule computing theory. A new method of constructing quotient space according to the structure is also presented in this paper. The differences between the quotient structure and structure-based method are proved. Finally, some examples show the rationality and feasibility of our methods. © 2005 IEEE.

关键词： Data processing

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A novel distributed machine learning method for classification: Parallel covering algorithm

A novel distributed machine learning method for classificati...

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7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012

作者： Zhang, Yanping Wang, Yuehua Zhao, Shu Anhui University Computer Science and Technology Institute Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei Anhui Province 230039 China

ISBN: (纸本)9783642318993

In this paper, we propose a novel distributed machine learning method: Parallel Covering Algorithm, which is inspired by the module feature of CA (Covering Algorithm). Classic method of CA is presented, and we analyze its independent part. Then we develop the Parallel CA by utilizing its modularity as well as data-set decomposition. Detailed implementation of the parallel computing process is described. In the experiment, three data sets are used to evaluate the Parallel CA, and the comparison with classic CA is also shown in the paper. Speedup and efficiency are two criterions to evaluate the performance of the algorithm. Both the analysis and the comparison indicate that the Parallel CA is more effective than CA. We also empirically compare the results obtained by Parallel SVM on a large data set, and it shows that our proposed algorithm is effective. © 2012 Springer-Verlag.

关键词： Learning algorithms

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