Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, 28: 380-390. 摘要 相位对于语音的感知质量很重要。 但是由于其中缺乏频谱时间结构,通过监督学习直接估计相位谱似乎很难。
Cho K, Van Merriënboer B, Bahdanau D et al (2014) On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 Chung J, Gulcehre C, Cho K et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv prepri...
Remaining useful life prognosis based on ensemble long short-term memory neural network IEEE Transactions on Instrumentation and Measurement, 70 (2020), pp. 1-2 Oct 15 CrossrefView in ScopusGoogle Scholar [16] J Chen, H Jing, Y Chang, Q. Liu Gated recurrent unit based recurrent neural netwo...
2) is the same for these three networks. It does not need to know how the edges are connected to which pairs of nodes. Furthermore, the DNN is composed of gated recurrent units (GRU)20 which capture and process the temporal information in our data. The use of GRU is also necessary ...
Learning Complex Spectral Mapping with Gated Convolutional Recurrent Networks for Monaural Speech Enhancement This repository provides an implementation of the gated convolutional recurrent network (GCRN) for monaural speech enhancement, developed in"Learning complex spectral mapping with gated convolutional recurr...
Specifically, RNNs and their variants, including long short-term memory (LSTM)24 networks and gated recurrent units (GRU)25, exhibit excellent performance in predicting dynamics but require estimation of many parameters. In addition to these networks with a huge number of parameters for updating, ...
The paper concludes that Gated Recurrent Unit (GRU) (used for labeling) together with the FURIA algorithm (used for rule extraction) obtain the best results in their experiments. The comparison made in their paper is certainly of high academic value. However, the proposal requires an enormous ...
論文程式碼:https://paperswithcode.com/paper/dccrn-deep-complex-convolution-recurrent-1 引用:Hu Y,Liu Y,Lv S,et al. DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement[J]. arXiv preprint arXiv:2008.00264,2020. ...
Since popular RNN components such as LSTM andgated recurrent unit(GRU) have already been implemented in most of the frameworks, users do not need to care about the underlying implementations. However, if you want to significantly modify them or make a completely new algorithm and components, the...
seen in dynamic imaging are 3D koosh-ball or stack-of-stars12,14,15which would result in streaking undersampling artifacts for which a different trained network would be probably required. Different architectural choices, such as 3D recurrent convolutional networks or variational neural networks,...