7 citations · 23 across the 24 of their papers we have counts for
5 papers · 2 filters
Multi-channel Opus compression for far-field automatic speech recognition with a fixed bitrate budget
Lukas Drude, Jahn Heymann, Andreas Schwarz +1
Automatic speech recognition (ASR) in the cloud allows the use of larger models and more powerful multi-channel signal processing front-ends compared to on-device processing. Howev…
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…