20 citations · 24 across the 3 of their papers we have counts for
10 papers
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…
Self-supervised Learning for Speech Enhancement
Yu-Che Wang, Shrikant Venkataramani, Paris Smaragdis
Supervised learning for single-channel speech enhancement requires carefully labeled training examples where the noisy mixture is input into the network and the network is trained…
Efficient Trainable Front-Ends for Neural Speech Enhancement
Jonah Casebeer, Umut Isik, Shrikant Venkataramani +1
Many neural speech enhancement and source separation systems operate in the time-frequency domain. Such models often benefit from making their Short-Time Fourier Transform (STFT) f…
End-to-end Non-Negative Autoencoders for Sound Source Separation
Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis
Discriminative models for source separation have recently been shown to produce impressive results. However, when operating on sources outside of the training set, these models can…
A Style Transfer Approach to Source Separation
Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis
Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output tha…
Class-conditional embeddings for music source separation
Prem Seetharaman, Gordon Wichern, Shrikant Venkataramani +1
Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep…