为解决网络训练复杂度高的问题并改进语音情感特征提取,提出了基于双嵌套残差网络(DNResNet11)与通道注意残差网络(CRNet)的双支路特征提取模型。首先,设计了低复杂度的DN-ResNet11以高效提取语谱图的融合情感特征,提升情感识别率;然后...为解决网络训练复杂度高的问题并改进语音情感特征提取,提出了基于双嵌套残差网络(DNResNet11)与通道注意残差网络(CRNet)的双支路特征提取模型。首先,设计了低复杂度的DN-ResNet11以高效提取语谱图的融合情感特征,提升情感识别率;然后,结合多尺度引导滤波和局部二值模式(local binary pattern,LBP)算法对语谱图进行细节增强;最后,融合两组特征进行情感分类,形成双支路加权融合模型(weighted fusion model based on dual nested residual and channel residual network,WFDN_CRNet),进一步提升情感表征能力。在CASIA、EMO-DB、IEMOCAP等语音情感数据集上情感识别率分别达到94.58%、85.59%、65.72%,所提方法在情感识别率优于ResNet18等基准方法的同时,显著降低了计算成本,验证了模型的有效性。展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
针对海量废弃家电回收图像数据在回收技术中难以有效利用的问题,提出了一种基于ResNet和多尺度卷积的废弃家电回收图像分类模型(Multi-scale and Efficient ResNet,ME-ResNet)。首先,基于残差结构设计了多尺度卷积模块以提升不同尺度特...针对海量废弃家电回收图像数据在回收技术中难以有效利用的问题,提出了一种基于ResNet和多尺度卷积的废弃家电回收图像分类模型(Multi-scale and Efficient ResNet,ME-ResNet)。首先,基于残差结构设计了多尺度卷积模块以提升不同尺度特征信息提取能力,在此基础上基于ResNet设计了针对废弃家电回收图像分类问题的ME-ResNet模型;其次,通过用深度可分离卷积替换多尺度卷积中的部分卷积层,实现ME-ResNet模型轻量化;最后,通过与其他卷积神经网络的对比实验,对ME-ResNet及其轻量化模型的性能进行了验证。研究结果表明:相较于经典的卷积神经网络ResNet34,ME-ResNet及其轻量化模型均能有效提升识别准确度,针对构建的数据集,其最优准确率分别提升了1.2%和0.3%,宏精确率分别提升了1.7%和0.9%,宏召回率分别提升了1.3%和0.2%,宏F1分数分别提升了1.5%和0.5%。展开更多
文摘为解决网络训练复杂度高的问题并改进语音情感特征提取,提出了基于双嵌套残差网络(DNResNet11)与通道注意残差网络(CRNet)的双支路特征提取模型。首先,设计了低复杂度的DN-ResNet11以高效提取语谱图的融合情感特征,提升情感识别率;然后,结合多尺度引导滤波和局部二值模式(local binary pattern,LBP)算法对语谱图进行细节增强;最后,融合两组特征进行情感分类,形成双支路加权融合模型(weighted fusion model based on dual nested residual and channel residual network,WFDN_CRNet),进一步提升情感表征能力。在CASIA、EMO-DB、IEMOCAP等语音情感数据集上情感识别率分别达到94.58%、85.59%、65.72%,所提方法在情感识别率优于ResNet18等基准方法的同时,显著降低了计算成本,验证了模型的有效性。
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
文摘针对海量废弃家电回收图像数据在回收技术中难以有效利用的问题,提出了一种基于ResNet和多尺度卷积的废弃家电回收图像分类模型(Multi-scale and Efficient ResNet,ME-ResNet)。首先,基于残差结构设计了多尺度卷积模块以提升不同尺度特征信息提取能力,在此基础上基于ResNet设计了针对废弃家电回收图像分类问题的ME-ResNet模型;其次,通过用深度可分离卷积替换多尺度卷积中的部分卷积层,实现ME-ResNet模型轻量化;最后,通过与其他卷积神经网络的对比实验,对ME-ResNet及其轻量化模型的性能进行了验证。研究结果表明:相较于经典的卷积神经网络ResNet34,ME-ResNet及其轻量化模型均能有效提升识别准确度,针对构建的数据集,其最优准确率分别提升了1.2%和0.3%,宏精确率分别提升了1.7%和0.9%,宏召回率分别提升了1.3%和0.2%,宏F1分数分别提升了1.5%和0.5%。
