In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended spee...In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.展开更多
采用局部均值分解(local mean decomposition,LMD)算法对电能质量扰动进行检测时存在'端点效应'和'模态混叠'问题,严重影响了检测精度。文章针对分布式电源接入引起的微电网电能质量问题,对LMD算法进行改进,提出四点波...采用局部均值分解(local mean decomposition,LMD)算法对电能质量扰动进行检测时存在'端点效应'和'模态混叠'问题,严重影响了检测精度。文章针对分布式电源接入引起的微电网电能质量问题,对LMD算法进行改进,提出四点波形曲率延拓,寻找最优匹配波形用以改善'端点效应'。采用三次样条函数插值提高计算速度,使得筛选过程更快,间接减小了'端点效应'和'模态混叠'的影响。进一步提出了自适应筛选停止准则,通过内外层循环判据确定筛选停止条件,从而抑制'模态混叠'。通过对单一扰动、复合扰动模拟信号与实测信号的时频分析,验证了所提算法的可行性和有效性。最后通过与其他算法的计算量对比分析,进一步表明所提算法具有较低的计算量。展开更多
文摘In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.
文摘采用局部均值分解(local mean decomposition,LMD)算法对电能质量扰动进行检测时存在'端点效应'和'模态混叠'问题,严重影响了检测精度。文章针对分布式电源接入引起的微电网电能质量问题,对LMD算法进行改进,提出四点波形曲率延拓,寻找最优匹配波形用以改善'端点效应'。采用三次样条函数插值提高计算速度,使得筛选过程更快,间接减小了'端点效应'和'模态混叠'的影响。进一步提出了自适应筛选停止准则,通过内外层循环判据确定筛选停止条件,从而抑制'模态混叠'。通过对单一扰动、复合扰动模拟信号与实测信号的时频分析,验证了所提算法的可行性和有效性。最后通过与其他算法的计算量对比分析,进一步表明所提算法具有较低的计算量。
