Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improv...Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.展开更多
The photonic neural processing unit(PNPU)demonstrates ultrahigh inference speed with low energy consumption,and it has become a promising hardware artificial intelligence(AI)accelerator.However,the nonidealities of th...The photonic neural processing unit(PNPU)demonstrates ultrahigh inference speed with low energy consumption,and it has become a promising hardware artificial intelligence(AI)accelerator.However,the nonidealities of the photonic device and the peripheral circuit make the practical application much more complex.Rather than optimizing the photonic device,the architecture,and the algorithm individually,a joint device-architecture-algorithm codesign method is proposed to improve the accuracy,efficiency and robustness of the PNPU.First,a full-flow simulator for the PNPU is developed from the back end simulator to the high-level training framework;Second,the full system architecture and the complete photonic chip design enable the simulator to closely model the real system;Third,the nonidealities of the photonic chip are evaluated for the PNPU design.The average test accuracy exceeds 98%,and the computing power exceeds 100TOPS.展开更多
设计了一款基于电磁带隙结构EBG(Electromagnetic Band Gap)的微带阵列天线,利用其周期性结构和禁带的特性,有效降低天线阵列单元间的互耦。由于采用单个EBG或单个周期,天线隔离度对贴片单元的尺寸和单元间的距离非常敏感。利用这一特性...设计了一款基于电磁带隙结构EBG(Electromagnetic Band Gap)的微带阵列天线,利用其周期性结构和禁带的特性,有效降低天线阵列单元间的互耦。由于采用单个EBG或单个周期,天线隔离度对贴片单元的尺寸和单元间的距离非常敏感。利用这一特性,分别对五单元、六单元的EBG结构对阵列天线的影响进行分析对比,最终得到最优方案,并运用六单元三周期的EBG结构,使天线间的隔离度显著改善,为以后紧凑型天线的研究奠定了基础,具有一定的参考价值。展开更多
Evolutionary neural network(ENN)shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces.It is one of the most efficient and adaptive optimizat...Evolutionary neural network(ENN)shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces.It is one of the most efficient and adaptive optimization techniques used widely to provide candidate solutions that lead to the fitness of the problem.ENN has the extraordinary ability to search the global and learning the approximate optimal solution regardless of the gradient information of the error functions.However,ENN requires high computation and processing which requires parallel processing platforms such as field programmable gate arrays(FPGAs)and graphic processing units(GPUs)to achieve a good performance.This work involves different new implementations of ENN by exploring and adopting different techniques and opportunities for parallel processing.Different versions of ENN algorithm have also been implemented and parallelized on FPGAs platform for low latency by exploiting the parallelism and pipelining approaches.Real data form mass spectrometry data(MSD)application was tested to examine and verify our implementations.This is a very important and extensive computation application which needs to search and find the optimal features(peaks)in MSD in order to distinguish cancer patients from control patients.ENN algorithm is also implemented and parallelized on single core and GPU platforms for comparison purposes.The computation time of our optimized algorithm on FPGA and GPU has been improved by a factor of 6.75 and 6,respectively.展开更多
基金This work is supported by Nanjing Institute of Technology(NIT)fund for Research Startup Projects of Introduced talents under Grant No.YKJ202019Nature Sci-ence Research Project of Higher Education Institutions in Jiangsu Province under Grant No.21KJB510018+1 种基金National Nature Science Foundation of China(NSFC)under Grant No.62001215NIT fund for Doctoral Research Projects under Grant No.ZKJ2020003.
文摘Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.
基金supported by the National Natural Science Foundation of China(Grant No.61827817)。
文摘The photonic neural processing unit(PNPU)demonstrates ultrahigh inference speed with low energy consumption,and it has become a promising hardware artificial intelligence(AI)accelerator.However,the nonidealities of the photonic device and the peripheral circuit make the practical application much more complex.Rather than optimizing the photonic device,the architecture,and the algorithm individually,a joint device-architecture-algorithm codesign method is proposed to improve the accuracy,efficiency and robustness of the PNPU.First,a full-flow simulator for the PNPU is developed from the back end simulator to the high-level training framework;Second,the full system architecture and the complete photonic chip design enable the simulator to closely model the real system;Third,the nonidealities of the photonic chip are evaluated for the PNPU design.The average test accuracy exceeds 98%,and the computing power exceeds 100TOPS.
文摘设计了一款基于电磁带隙结构EBG(Electromagnetic Band Gap)的微带阵列天线,利用其周期性结构和禁带的特性,有效降低天线阵列单元间的互耦。由于采用单个EBG或单个周期,天线隔离度对贴片单元的尺寸和单元间的距离非常敏感。利用这一特性,分别对五单元、六单元的EBG结构对阵列天线的影响进行分析对比,最终得到最优方案,并运用六单元三周期的EBG结构,使天线间的隔离度显著改善,为以后紧凑型天线的研究奠定了基础,具有一定的参考价值。
文摘Evolutionary neural network(ENN)shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces.It is one of the most efficient and adaptive optimization techniques used widely to provide candidate solutions that lead to the fitness of the problem.ENN has the extraordinary ability to search the global and learning the approximate optimal solution regardless of the gradient information of the error functions.However,ENN requires high computation and processing which requires parallel processing platforms such as field programmable gate arrays(FPGAs)and graphic processing units(GPUs)to achieve a good performance.This work involves different new implementations of ENN by exploring and adopting different techniques and opportunities for parallel processing.Different versions of ENN algorithm have also been implemented and parallelized on FPGAs platform for low latency by exploiting the parallelism and pipelining approaches.Real data form mass spectrometry data(MSD)application was tested to examine and verify our implementations.This is a very important and extensive computation application which needs to search and find the optimal features(peaks)in MSD in order to distinguish cancer patients from control patients.ENN algorithm is also implemented and parallelized on single core and GPU platforms for comparison purposes.The computation time of our optimized algorithm on FPGA and GPU has been improved by a factor of 6.75 and 6,respectively.
