| End-to-end source separation with adaptive front-ends S Venkataramani, J Casebeer, P Smaragdis Asilomar, 2018 | 47 | 2018 |
| A neural network alternative to non-negative audio models P Smaragdis, S Venkataramani 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 46 | 2017 |
| Adaptive front-ends for end-to-end source separation S Venkataramani, J Casebeer, P Smaragdis Proc. NIPS, 2017 | 32 | 2017 |
| Unsupervised deep clustering for source separation: Direct learning from mixtures using spatial information E Tzinis, S Venkataramani, P Smaragdis ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 23 | 2019 |
| Class-conditional embeddings for music source separation P Seetharaman, G Wichern, S Venkataramani, J Le Roux ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 17 | 2019 |
| Performance Based Cost Functions for End-to-End Speech Separation S Venkataramani, R Higa, P Smaragdis Asia-Pacific Signal and Information Processing Association Annual Summit and …, 2018 | 15 | 2018 |
| Two-step sound source separation: Training on learned latent targets E Tzinis, S Venkataramani, Z Wang, C Subakan, P Smaragdis ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 13 | 2020 |
| Neural network alternatives to convolutive audio models for source separation S Venkataramani, C Subakan, P Smaragdis 2017 IEEE 27th International Workshop on Machine Learning for Signal …, 2017 | 11 | 2017 |
| End-to-end networks for supervised single-channel speech separation S Venkataramani, P Smaragdis arXiv preprint arXiv:1810.02568, 2018 | 9 | 2018 |
| Vocal Separation using Singer-Vowel Priors Obtained from Polyphonic Audio. S Venkataramani, N Nayak, P Rao, R Velmurugan ISMIR, 283-288, 2014 | 4 | 2014 |
| End-to-end non-negative autoencoders for sound source separation S Venkataramani, E Tzinis, P Smaragdis ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 2 | 2020 |
| A style transfer approach to source separation S Venkataramani, E Tzinis, P Smaragdis 2019 IEEE Workshop on Applications of Signal Processing to Audio and …, 2019 | 2 | 2019 |
| Self-supervised Learning for Speech Enhancement YC Wang, S Venkataramani, P Smaragdis arXiv preprint arXiv:2006.10388, 2020 | 1 | 2020 |
| End-to-end non-negative auto-encoders: a deep neural alternative to non-negative audio modeling S Venkataramani University of Illinois at Urbana-Champaign, 2020 | | 2020 |
| Efficient Trainable Front-Ends for Neural Speech Enhancement J Casebeer, U Isik, S Venkataramani, A Krishnaswamy ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | | 2020 |
| Two-Step Sound Source Separation: Training on Learned Latent Targets (Presentation) E Tzinis, S Venkataramani, Z Wang, C Subakan, P Smaragdis | | 2020 |
| Automatic voiceover correction system S Venkataramani, P Smaragdis, G Mysore US Patent 10,453,475, 2019 | | 2019 |
| Unsuper vised Deep Clustering for Source Separation: Direct Learning from Mixtures Using Spatial Information Slides E Tzinis, S Venkataramani, P Smaragdis | | 2019 |
| AutoDub: Automatic Redubbing for Voiceover Editing S Venkataramani, P Smaragdis, G Mysore Proceedings of the 30th Annual ACM Symposium on User Interface Software and …, 2017 | | 2017 |
| Improving mobile phone based query recognition with a microphone array S Venkataramani, R Velmurugan, P Rao 2014 Twentieth National Conference on Communications (NCC), 1-6, 2014 | | 2014 |
Paris SmaragdisUniversity of Illinois at Urbana-Champaign / Adobe ResearchVerified email at illinois.edu
