7 citations · 18 across the 14 of their papers we have counts for
5 papers · 1 filter
Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement
Ritwik Giri, Shrikant Venkataramani, Jean-Marc Valin +2
The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size a…
Semi-Supervised Singing Voice Separation with Noisy Self-Training
Zhepei Wang, Ritwik Giri, Umut Isik +2
Recent progress in singing voice separation has primarily focused on supervised deep learning methods. However, the scarcity of ground-truth data with clean musical sources has bee…
Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
Jonah Casebeer, Vinjai Vale, Umut Isik +3
Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output…
Low-Complexity, Real-Time Joint Neural Echo Control and Speech Enhancement Based On PercepNet
Jean-Marc Valin, Srikanth Tenneti, Karim Helwani +2
Speech enhancement algorithms based on deep learning have greatly surpassed their traditional counterparts and are now being considered for the task of removing acoustic echo from…
Enhancing Audio Augmentation Methods with Consistency Learning
Turab Iqbal, Karim Helwani, Arvindh Krishnaswamy +1
Data augmentation is an inexpensive way to increase training data diversity and is commonly achieved via transformations of existing data. For tasks such as classification, there i…