| Non-negative matrix factorization for polyphonic music transcription P Smaragdis, JC Brown 2003 IEEE Workshop on Applications of Signal Processing to Audio and …, 2003 | 1318 | 2003 |
| Blind separation of convolved mixtures in the frequency domain P Smaragdis Neurocomputing 22 (1-3), 21-34, 1998 | 1081 | 1998 |
| Deep learning for monaural speech separation PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 582 | 2014 |
| Joint optimization of masks and deep recurrent neural networks for monaural source separation PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (12 …, 2015 | 577 | 2015 |
| Convolutive speech bases and their application to supervised speech separation P Smaragdis IEEE Transactions on Audio, Speech, and Language Processing 15 (1), 1-12, 2006 | 511 | 2006 |
| Supervised and unsupervised speech enhancement using nonnegative matrix factorization N Mohammadiha, P Smaragdis, A Leijon IEEE Transactions on audio, speech, and language processing 21 (10), 2140-2151, 2013 | 506 | 2013 |
| Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs P Smaragdis International Conference on Independent Component Analysis and Signal …, 2004 | 486 | 2004 |
| Singing-voice separation from monaural recordings using robust principal component analysis PS Huang, SD Chen, P Smaragdis, M Hasegawa-Johnson 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 462 | 2012 |
| Speech denoising using nonnegative matrix factorization with priors KW Wilson, B Raj, P Smaragdis, A Divakaran 2008 IEEE International Conference on Acoustics, Speech and Signal …, 2008 | 381 | 2008 |
| Bitwise neural networks M Kim, P Smaragdis arXiv preprint arXiv:1601.06071, 2016 | 344 | 2016 |
| Supervised and semi-supervised separation of sounds from single-channel mixtures P Smaragdis, B Raj, M Shashanka International Conference on Independent Component Analysis and Signal …, 2007 | 328 | 2007 |
| Audio analysis for surveillance applications R Radhakrishnan, A Divakaran, A Smaragdis IEEE Workshop on Applications of Signal Processing to Audio and Acoustics …, 2005 | 290 | 2005 |
| Static and dynamic source separation using nonnegative factorizations: A unified view P Smaragdis, C Fevotte, GJ Mysore, N Mohammadiha, M Hoffman IEEE Signal Processing Magazine 31 (3), 66-75, 2014 | 238 | 2014 |
| Sudo rm-rf: Efficient networks for universal audio source separation E Tzinis, Z Wang, P Smaragdis 2020 IEEE 30th International Workshop on Machine Learning for Signal …, 2020 | 212 | 2020 |
| A probabilistic latent variable model for acoustic modeling P Smaragdis, B Raj, M Shashanka Advances in models for acoustic processing, NIPS 148, 8-1, 2006 | 212 | 2006 |
| Evaluation of blind signal separation methods DWE Schobben, K Torkkola, P Smaragdis conference; ICA'99, International Workshop on Independent Component Analysis …, 1999 | 205 | 1999 |
| Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks. PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis ISMIR, 477-482, 2014 | 198 | 2014 |
| Experiments on deep learning for speech denoising. D Liu, P Smaragdis, M Kim Interspeech, 2685-2689, 2014 | 174 | 2014 |
| Non-negative hidden Markov modeling of audio with application to source separation GJ Mysore, P Smaragdis, B Raj International Conference on Latent Variable Analysis and Signal Separation …, 2010 | 167 | 2010 |
| Probabilistic latent variable models as nonnegative factorizations M Shashanka, B Raj, P Smaragdis Computational intelligence and neuroscience 2008 (1), 947438, 2008 | 165 | 2008 |
Bhiksha RajCarnegie Mellon UniversityVerified email at cs.cmu.edu
