给我推荐20个比较流行的人声分离深度学习算法模型

好的，在这里推荐您20个比较流行的人声分离深度学习算法模型：

1. 深度循环网络(Deep Recurrent Network, DRN)
2. 连续时间建模(Continuous Time Modeling, CTM)
3. 音频信号段的深度自动编码器(Deep Autoencoder for Audio Segment, DAE)
4. 声学注意力机制(Acoustic Attention Mechanism, AAM)
5. 基于频谱的卷积神经网络(Spectrogram-based Convolutional Neural Network, SCNN)
6. 基于多源信息的深度学习(Deep Learning Based on Multi-source Information, DLBMI)
7. 基于时间建模的深度卷积网络(Deep Convolutional Network Based on Time Modeling, DCNTM)
8. 音频信号的深度独立子空间建模(Deep Independent Subspace Modeling for Audio Signals, DISMAS)
9. 基于声学特征的深度卷积网络(Deep Convolutional Network Based on Acoustic Features, DCNAF)
10. 基于时域信号的深度稠密网络(Deep Dense Network Based on Time Domain Signals, DDNTDS)
11. 音频信号的深度学习时域建模(Deep Learning Time Domain Modeling for Audio Signals, DLTDMAS)
12. 基于频谱的深度稀疏编码(Deep Sparse Coding Based on Spectrogram, DSCCS)
13. 基于时频建模的深度学习(Deep Learning Based on Time-Frequency Modeling, DLBTFM)
14. 音频信号的深度自动编码器(Deep Autoencoder for Audio Signals, DAAS)
15. 基于时频分析的深度网络(Deep Network Based on Time-Frequency Analysis, DNBTF)
16. 基于时频域的深度卷积网络(Deep Convolutional Network Based on Time-F