30 citations · 50 across the 9 of their papers we have counts for
14 papers
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion
Yinghao Aaron Li, Ali Zare, Nima Mesgarani
We present an unsupervised non-parallel many-to-many voice conversion (VC) method using a generative adversarial network (GAN) called StarGAN v2. Using a combination of adversarial…
Speaker and Direction Inferred Dual-channel Speech Separation
Chenxing Li, Jiaming Xu, Nima Mesgarani +1
Most speech separation methods, trying to separate all channel sources simultaneously, are still far from having enough general- ization capabilities for real scenarios where the n…
Continuous Speech Separation Using Speaker Inventory for Long Multi-talker Recording
Cong Han, Yi Luo, Chenda Li +8
Leveraging additional speaker information to facilitate speech separation has received increasing attention in recent years. Recent research includes extracting target speech by us…
Group Communication with Context Codec for Lightweight Source Separation
Yi Luo, Cong Han, Nima Mesgarani
Despite the recent progress on neural network architectures for speech separation, the balance between the model size, model complexity and model performance is still an important…
Ultra-Lightweight Speech Separation via Group Communication
Yi Luo, Cong Han, Nima Mesgarani
Model size and complexity remain the biggest challenges in the deployment of speech enhancement and separation systems on low-resource devices such as earphones and hearing aids. A…
Implicit Filter-and-sum Network for Multi-channel Speech Separation
Yi Luo, Nima Mesgarani
Various neural network architectures have been proposed in recent years for the task of multi-channel speech separation. Among them, the filter-and-sum network (FaSNet) performs en…