本文提出一种利用双解码卷积循环网络(Dual-decoder Convolutional Recurrent Network,DCRN)代替FxLMS(Filtered-x Least Mean Square)算法的有源噪声控制方法,考虑到相位信息在有源噪声控制(Active Noise Control,ANC)中的重要性,DCRN...本文提出一种利用双解码卷积循环网络(Dual-decoder Convolutional Recurrent Network,DCRN)代替FxLMS(Filtered-x Least Mean Square)算法的有源噪声控制方法,考虑到相位信息在有源噪声控制(Active Noise Control,ANC)中的重要性,DCRN网络的输入特征为噪声信号的复数频谱(包括实部谱和虚部谱).网络结构中,采用编码模块从噪声复数频谱中提取特征,利用双解码模块分别估计网络输出的实部谱和虚部谱,采用参数共享机制和组策略以降低训练参数的数量并提高网络的学习能力和泛化能力.特别是针对风噪声,选用新的损失函数以及对训练数据进行正则化处理以提升DCRN的性能.实验结果表明,DCRN方法在仿真环境与有源降噪耳机环境下对一般噪声和风噪声都表现出良好的降噪性能和鲁棒性.展开更多
The enhanced variable rate codec (EVRC) is a standard for the 'Speech ServiceOption 3 for Wideband Spread Spectrum Digital System,' which has been employed in both IS-95cellular systems and ANSI J-STC-008 PCS ...The enhanced variable rate codec (EVRC) is a standard for the 'Speech ServiceOption 3 for Wideband Spread Spectrum Digital System,' which has been employed in both IS-95cellular systems and ANSI J-STC-008 PCS (personal communications systems). This paper concentrateson channel decoders that exploit the residual redundancy inherent in the enhanced variable ratecodec bitstream. This residual redundancy is quantified by modeling the parameters as first orderMarkov chains and computing the entropy rate based on the relative frequencies of transitions.Moreover, this residual redundancy can be exploited by an appropriately 'tuned' channel decoder toprovide substantial coding gain when compared with the decoders that do not exploit it. Channelcoding schemes include convolutional codes, and iteratively decoded parallel concatenatedconvolutional 'turbo' codes.展开更多
Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significanc...Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.展开更多
文摘本文提出一种利用双解码卷积循环网络(Dual-decoder Convolutional Recurrent Network,DCRN)代替FxLMS(Filtered-x Least Mean Square)算法的有源噪声控制方法,考虑到相位信息在有源噪声控制(Active Noise Control,ANC)中的重要性,DCRN网络的输入特征为噪声信号的复数频谱(包括实部谱和虚部谱).网络结构中,采用编码模块从噪声复数频谱中提取特征,利用双解码模块分别估计网络输出的实部谱和虚部谱,采用参数共享机制和组策略以降低训练参数的数量并提高网络的学习能力和泛化能力.特别是针对风噪声,选用新的损失函数以及对训练数据进行正则化处理以提升DCRN的性能.实验结果表明,DCRN方法在仿真环境与有源降噪耳机环境下对一般噪声和风噪声都表现出良好的降噪性能和鲁棒性.
文摘The enhanced variable rate codec (EVRC) is a standard for the 'Speech ServiceOption 3 for Wideband Spread Spectrum Digital System,' which has been employed in both IS-95cellular systems and ANSI J-STC-008 PCS (personal communications systems). This paper concentrateson channel decoders that exploit the residual redundancy inherent in the enhanced variable ratecodec bitstream. This residual redundancy is quantified by modeling the parameters as first orderMarkov chains and computing the entropy rate based on the relative frequencies of transitions.Moreover, this residual redundancy can be exploited by an appropriately 'tuned' channel decoder toprovide substantial coding gain when compared with the decoders that do not exploit it. Channelcoding schemes include convolutional codes, and iteratively decoded parallel concatenatedconvolutional 'turbo' codes.
基金Shanxi Provincial Key Research and Development Program Project Fund(No.201703D111011)。
文摘Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.
