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ORCIDParis Smaragdis
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- affiliation: MIT, Cambridge, USA
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2020 – today
- 2023
[j27]Jonah Casebeer
, Nicholas J. Bryan, Paris Smaragdis:
Meta-AF: Meta-Learning for Adaptive Filters. IEEE ACM Trans. Audio Speech Lang. Process. 31: 355-370 (2023)- [i47]Zhepei Wang, Ritwik Giri, Devansh Shah, Jean-Marc Valin, Michael M. Goodwin, Paris Smaragdis:
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement. CoRR abs/2302.11768 (2023) - 2022
- [j26]Efthymios Tzinis, Yossi Adi, Vamsi K. Ithapu, Buye Xu, Paris Smaragdis, Anurag Kumar:
RemixIT: Continual Self-Training of Speech Enhancement Models via Bootstrapped Remixing. IEEE J. Sel. Top. Signal Process. 16(6): 1329-1341 (2022) - [j25]Zhepei Wang, Cem Subakan, Xilin Jiang, Junkai Wu, Efthymios Tzinis, Mirco Ravanelli, Paris Smaragdis:
Learning Representations for New Sound Classes With Continual Self-Supervised Learning. IEEE Signal Process. Lett. 29: 2607-2611 (2022) - [j24]Efthymios Tzinis, Zhepei Wang, Xilin Jiang, Paris Smaragdis:
Compute and Memory Efficient Universal Sound Source Separation. J. Signal Process. Syst. 94(2): 245-259 (2022) - [c105]Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy:
Neural Speech Synthesis on a Shoestring: Improving the Efficiency of Lpcnet. ICASSP 2022: 8437-8441 - [c104]Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery, Timothy B. Terriberry, Michael Klingbeil, Paris Smaragdis, Arvindh Krishnaswamy:
Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model. INTERSPEECH 2022: 570-574 - [c103]Krishna Subramani, Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy:
End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation. INTERSPEECH 2022: 818-822 - [c102]Efthymios Tzinis, Gordon Wichern, Aswin Shanmugam Subramanian, Paris Smaragdis, Jonathan Le Roux:
Heterogeneous Target Speech Separation. INTERSPEECH 2022: 1796-1800 - [c101]Austin Lu, Ethaniel Moore, Arya Nallanthighall, Kanad Sarkar, Manan Mittal, Ryan M. Corey, Paris Smaragdis, Andrew C. Singer:
Mechatronic Generation of Datasets for Acoustics Research. IWAENC 2022: 1-5 - [c100]Junkai Wu, Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis:
Meta-Learning for Adaptive Filters with higher-order Frequency Dependencies. IWAENC 2022: 1-5 - [i46]Efthymios Tzinis, Yossi Adi, Vamsi Krishna Ithapu, Buye Xu, Paris Smaragdis, Anurag Kumar:
RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing. CoRR abs/2202.08862 (2022) - [i45]Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy:
Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet. CoRR abs/2202.11169 (2022) - [i44]Krishna Subramani, Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy:
End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation. CoRR abs/2202.11301 (2022) - [i43]Efthymios Tzinis, Gordon Wichern, Aswin Shanmugam Subramanian, Paris Smaragdis, Jonathan Le Roux:
Heterogeneous Target Speech Separation. CoRR abs/2204.03594 (2022) - [i42]Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis:
Meta-AF: Meta-Learning for Adaptive Filters. CoRR abs/2204.11942 (2022) - [i41]Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery, Timothy B. Terriberry, Michael Klingbeil, Paris Smaragdis, Arvindh Krishnaswamy:
Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model. CoRR abs/2205.05785 (2022) - [i40]Zhepei Wang, Cem Subakan, Xilin Jiang, Junkai Wu, Efthymios Tzinis, Mirco Ravanelli, Paris Smaragdis:
Learning Representations for New Sound Classes With Continual Self-Supervised Learning. CoRR abs/2205.07390 (2022) - [i39]Zhepei Wang, Ritwik Giri, Shrikant Venkataramani, Umut Isik, Jean-Marc Valin, Paris Smaragdis, Michael M. Goodwin, Arvindh Krishnaswamy:
Semi-supervised Time Domain Target Speaker Extraction with Attention. CoRR abs/2206.09072 (2022) - [i38]Anku Adhikari, Samuel Guo, Paris Smaragdis, Marianne Winslett:
Don't Look Up: Ubiquitous Data Exfiltration Pathways in Commercial Spaces. CoRR abs/2206.12944 (2022) - [i37]Junkai Wu, Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis:
Meta-Learning for Adaptive Filters with Higher-Order Frequency Dependencies. CoRR abs/2209.09955 (2022) - [i36]Efthymios Tzinis, Gordon Wichern, Paris Smaragdis, Jonathan Le Roux:
Optimal Condition Training for Target Source Separation. CoRR abs/2211.05927 (2022) - [i35]Dimitrios Bralios, Efthymios Tzinis, Gordon Wichern, Paris Smaragdis, Jonathan Le Roux:
Latent Iterative Refinement for Modular Source Separation. CoRR abs/2211.11917 (2022) - [i34]Ahmed Mustafa, Jean-Marc Valin, Jan Büthe, Paris Smaragdis, Mike Goodwin:
Framewise WaveGAN: High Speed Adversarial Vocoder in Time Domain with Very Low Computational Complexity. CoRR abs/2212.04532 (2022) - 2021
- [c99]Marco A. Martínez Ramírez, Oliver Wang, Paris Smaragdis, Nicholas J. Bryan:
Differentiable Signal Processing With Black-Box Audio Effects. ICASSP 2021: 66-70 - [c98]Efthymios Tzinis, Dimitrios Bralios, Paris Smaragdis:
Unified Gradient Reweighting for Model Biasing with Applications to Source Separation. ICASSP 2021: 531-535 - [c97]An Zhao, Krishna Subramani, Paris Smaragdis:
Optimizing Short-Time Fourier Transform Parameters via Gradient Descent. ICASSP 2021: 736-740 - [c96]Jonah Casebeer, Jamshed Kaikaus, Paris Smaragdis:
Communication-Cost Aware Microphone Selection for Neural Speech Enhancement with Ad-Hoc Microphone Arrays. ICASSP 2021: 8438-8442 - [c95]Krishna Subramani, Paris Smaragdis:
Point Cloud Audio Processing. WASPAA 2021: 31-35 - [c94]Zhepei Wang, Jonah Casebeer, Adam Clemmitt, Efthymios Tzinis, Paris Smaragdis:
Sound Event Detection with Adaptive Frequency Selection. WASPAA 2021: 41-45 - [c93]Efthymios Tzinis, Jonah Casebeer, Zhepei Wang, Paris Smaragdis:
Separate But Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data. WASPAA 2021: 46-50 - [c92]Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis:
Auto-DSP: Learning to Optimize Acoustic Echo Cancellers. WASPAA 2021: 291-295 - [i33]Efthymios Tzinis, Zhepei Wang, Xilin Jiang, Paris Smaragdis:
Compute and memory efficient universal sound source separation. CoRR abs/2103.02644 (2021) - [i32]Krishna Subramani, Paris Smaragdis:
Point Cloud Audio Processing. CoRR abs/2105.02469 (2021) - [i31]Efthymios Tzinis, Jonah Casebeer, Zhepei Wang, Paris Smaragdis:
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data. CoRR abs/2105.04727 (2021) - [i30]Marco A. Martínez Ramírez, Oliver Wang, Paris Smaragdis, Nicholas J. Bryan:
Differentiable Signal Processing With Black-Box Audio Effects. CoRR abs/2105.04752 (2021) - [i29]Zhepei Wang, Jonah Casebeer, Adam Clemmitt, Efthymios Tzinis, Paris Smaragdis:
Sound Event Detection with Adaptive Frequency Selection. CoRR abs/2105.07596 (2021) - [i28]Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis:
Auto-DSP: Learning to Optimize Acoustic Echo Cancellers. CoRR abs/2110.04284 (2021) - 2020
- [c91]Efthymios Tzinis, Shrikant Venkataramani, Zhepei Wang, Y. Cem Sübakan, Paris Smaragdis:
Two-Step Sound Source Separation: Training On Learned Latent Targets. ICASSP 2020: 31-35 - [c90]Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis:
End-To-End Non-Negative Autoencoders for Sound Source Separation. ICASSP 2020: 116-120 - [c89]Stylianos I. Mimilakis, Nicholas J. Bryan, Paris Smaragdis:
One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization. ICASSP 2020: 256-260 - [c88]Efthymios Tzinis, Zhepei Wang, Paris Smaragdis:
Sudo RM -RF: Efficient Networks for Universal Audio Source Separation. MLSP 2020: 1-6 - [i27]Yu-Che Wang, Shrikant Venkataramani, Paris Smaragdis:
Self-supervised Learning for Speech Enhancement. CoRR abs/2006.10388 (2020) - [i26]Efthymios Tzinis, Zhepei Wang, Paris Smaragdis:
Sudo rm -rf: Efficient Networks for Universal Audio Source Separation. CoRR abs/2007.06833 (2020) - [i25]Efthymios Tzinis, Dimitrios Bralios, Paris Smaragdis:
Unified Gradient Reweighting for Model Biasing with Applications to Source Separation. CoRR abs/2010.13228 (2020) - [i24]An Zhao, Krishna Subramani, Paris Smaragdis:
Optimizing Short-Time Fourier Transform Parameters via Gradient Descent. CoRR abs/2010.15049 (2020) - [i23]Jonah Casebeer, Jamshed Kaikaus, Paris Smaragdis:
Communication-Cost Aware Microphone Selection For Neural Speech Enhancement with Ad-hoc Microphone Arrays. CoRR abs/2011.07348 (2020)
2010 – 2019
- 2019
- [c87]Prem Seetharaman, Gautham J. Mysore, Bryan Pardo, Paris Smaragdis, Celso Gomes:
VoiceAssist: Guiding Users to High-Quality Voice Recordings. CHI 2019: 309 - [c86]Efthymios Tzinis, Shrikant Venkataramani, Paris Smaragdis:
Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures Using Spatial Information. ICASSP 2019: 81-85 - [c85]Jonah Casebeer, Zhepei Wang, Paris Smaragdis:
Multi-view Networks for Multi-channel Audio Classification. ICASSP 2019: 940-944 - [c84]Dylan Fagot, Herwig Wendt, Cédric Févotte, Paris Smaragdis:
Majorization-minimization Algorithms for Convolutive NMF with the Beta-divergence. ICASSP 2019: 8202-8206 - [c83]Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis:
A Style Transfer Approach to Source Separation. WASPAA 2019: 170-174 - [c82]Jonah Casebeer, Michael Colomb, Paris Smaragdis:
Deep Tensor Factorization for Spatially-Aware Scene Decomposition. WASPAA 2019: 180-184 - [c81]Zhepei Wang, Y. Cem Sübakan, Efthymios Tzinis, Paris Smaragdis, Laurent Charlin:
Continual Learning of New Sound Classes Using Generative Replay. WASPAA 2019: 308-312 - [i22]Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis:
A Style Transfer Approach to Source Separation. CoRR abs/1905.00151 (2019) - [i21]Jonah Casebeer, Michael Colomb, Paris Smaragdis:
Deep Tensor Factorization for Spatially-Aware Scene Decomposition. CoRR abs/1905.01391 (2019) - [i20]Zhepei Wang, Y. Cem Sübakan, Efthymios Tzinis, Paris Smaragdis, Laurent Charlin:
Continual Learning of New Sound Classes using Generative Replay. CoRR abs/1906.00654 (2019) - [i19]Efthymios Tzinis, Shrikant Venkataramani, Zhepei Wang, Y. Cem Sübakan, Paris Smaragdis:
Two-Step Sound Source Separation: Training on Learned Latent Targets. CoRR abs/1910.09804 (2019) - [i18]Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis:
End-to-end Non-Negative Autoencoders for Sound Source Separation. CoRR abs/1911.00102 (2019) - 2018
- [c80]Shrikant Venkataramani, Jonah Casebeer, Paris Smaragdis:
End-To-End Source Separation With Adaptive Front-Ends. ACSSC 2018: 684-688 - [c79]Shrikant Venkataramani, Ryley Higa, Paris Smaragdis:
Performance Based Cost Functions for End-to-End Speech Separation. APSIPA 2018: 350-355 - [c78]Y. Cem Sübakan, Paris Smaragdis:
Generative Adversarial Source Separation. ICASSP 2018: 26-30 - [c77]Prem Seetharaman, Gautham J. Mysore, Paris Smaragdis, Bryan Pardo:
Blind Estimation of the Speech Transmission Index for Speech Quality Prediction. ICASSP 2018: 591-595 - [c76]Minje Kim, Paris Smaragdis:
Bitwise Neural Networks for Efficient Single-Channel Source Separation. ICASSP 2018: 701-705 - [c75]Jonah Casebeer, Brian Luc, Paris Smaragdis:
Multi-View Networks for Denoising of Arbitrary Numbers of Channels. IWAENC 2018: 496-500 - [p4]Gerald Friedland, Paris Smaragdis, Josh H. McDermott, Bhiksha Raj:
Audition for multimedia computing. Frontiers of Multimedia Research 2018: 31-50 - [i17]Y. Cem Sübakan, Oluwasanmi Koyejo, Paris Smaragdis:
Learning the Base Distribution in Implicit Generative Models. CoRR abs/1803.04357 (2018) - [i16]Shrikant Venkataramani, Ryley Higa, Paris Smaragdis:
Performance Based Cost Functions for End-to-End Speech Separation. CoRR abs/1806.00511 (2018) - [i15]Jonah Casebeer, Brian Luc, Paris Smaragdis:
Multi-View Networks for Denoising of Arbitrary Numbers of Channels. CoRR abs/1806.05296 (2018) - [i14]Shrikant Venkataramani, Paris Smaragdis:
End-to-end Networks for Supervised Single-channel Speech Separation. CoRR abs/1810.02568 (2018) - [i13]Jonah Casebeer, Zhepei Wang, Paris Smaragdis:
Multi-View Networks For Multi-Channel Audio Classification. CoRR abs/1811.01251 (2018) - [i12]Efthymios Tzinis, Shrikant Venkataramani, Paris Smaragdis:
Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information. CoRR abs/1811.01531 (2018) - 2017
- [c74]Paris Smaragdis, Shrikant Venkataramani:
A neural network alternative to non-negative audio models. ICASSP 2017: 86-90 - [c73]Shrikant Venkataramani, Y. Cem Sübakan, Paris Smaragdis:
Neural network alternatives toconvolutive audio models for source separation. MLSP 2017: 1-6 - [c72]Shrikant Venkataramani, Paris Smaragdis, Gautham J. Mysore:
AutoDub: Automatic Redubbing for Voiceover Editing. UIST 2017: 533-538 - [c71]Ralf Gunter Correa Carvalho, Paris Smaragdis:
Towards end-to-end polyphonic music transcription: Transforming music audio directly to a score. WASPAA 2017: 151-155 - [c70]Y. Cem Sübakan, Paris Smaragdis:
Diagonal rnns in symbolic music modeling. WASPAA 2017: 354-358 - [i11]Y. Cem Sübakan, Paris Smaragdis:
Diagonal RNNs in Symbolic Music Modeling. CoRR abs/1704.05420 (2017) - [i10]Shrikant Venkataramani, Paris Smaragdis:
End-to-end Source Separation with Adaptive Front-Ends. CoRR abs/1705.02514 (2017) - [i9]Nasser Mohammadiha, Paris Smaragdis, Ghazaleh Panahandeh, Simon Doclo:
A State-Space Approach to Dynamic Nonnegative Matrix Factorization. CoRR abs/1709.00025 (2017) - [i8]Nasser Mohammadiha, Paris Smaragdis, Arne Leijon:
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization. CoRR abs/1709.05362 (2017) - [i7]Shrikant Venkataramani, Y. Cem Sübakan, Paris Smaragdis:
Neural Network Alternatives to Convolutive Audio Models for Source Separation. CoRR abs/1709.07908 (2017) - [i6]Y. Cem Sübakan, Paris Smaragdis:
Generative Adversarial Source Separation. CoRR abs/1710.10779 (2017) - 2016
- [j23]Johannes Traa, David Wingate, Noah D. Stein, Paris Smaragdis:
Robust Source Localization and Enhancement With a Probabilistic Steered Response Power Model. IEEE ACM Trans. Audio Speech Lang. Process. 24(3): 493-503 (2016) - [c69]Minje Kim, Paris Smaragdis:
Efficient neighborhood-based topic modeling for collaborative audio enhancement on massive crowdsourced recordings. ICASSP 2016: 41-45 - [i5]Minje Kim, Paris Smaragdis:
Bitwise Neural Networks. CoRR abs/1601.06071 (2016) - [i4]Paris Smaragdis, Shrikant Venkataramani:
A Neural Network Alternative to Non-Negative Audio Models. CoRR abs/1609.03296 (2016) - [i3]Mohammad Babaeizadeh, Paris Smaragdis, Roy H. Campbell:
NoiseOut: A Simple Way to Prune Neural Networks. CoRR abs/1611.06211 (2016) - 2015
- [j22]Minje Kim, Paris Smaragdis:
Mixtures of Local Dictionaries for Unsupervised Speech Enhancement. IEEE Signal Process. Lett. 22(3): 288-292 (2015) - [j21]Tuomas Virtanen, Jort Florent Gemmeke, Bhiksha Raj, Paris Smaragdis:
Compositional Models for Audio Processing: Uncovering the structure of sound mixtures. IEEE Signal Process. Mag. 32(2): 125-144 (2015) - [j20]Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis:
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation. IEEE ACM Trans. Audio Speech Lang. Process. 23(12): 2136-2147 (2015) - [j19]Nasser Mohammadiha, Paris Smaragdis, Ghazaleh Panahandeh, Simon Doclo:
A State-Space Approach to Dynamic Nonnegative Matrix Factorization. IEEE Trans. Signal Process. 63(4): 949-959 (2015) - [j18]Saeid Sanei, Paris Smaragdis, Anthony T. S. Ho, Asoke K. Nandi, Jan Larsen:
Guest Editorial: Machine Learning for Signal Processing. J. Signal Process. Syst. 79(2): 113-116 (2015) - [c68]Minje Kim, Paris Smaragdis:
Adaptive Denoising Autoencoders: A Fine-Tuning Scheme to Learn from Test Mixtures. LVA/ICA 2015: 100-107 - [c67]Minje Kim, Paris Smaragdis, Gautham J. Mysore:
Efficient manifold preserving audio source separation using locality sensitive hashing. ICASSP 2015: 479-483 - [c66]Nasser Mohammadiha, Paris Smaragdis, Simon Doclo:
Joint acoustic and spectral modeling for speech dereverberation using non-negative representations. ICASSP 2015: 4410-4414 - [c65]Aki Nikolaidis, Drew Goatz, Paris Smaragdis, Arthur F. Kramer:
Predicting Skill-Based Task Performance and Learning with fMRI Motor and Subcortical Network Connectivity. PRNI 2015: 93-96 - [c64]Y. Cem Sübakan, Johannes Traa, Paris Smaragdis, Daniel J. Hsu:
Method of moments learning for left-to-right Hidden Markov models. WASPAA 2015: 1-5 - [c63]Johannes Traa, Paris Smaragdis, Noah D. Stein, David Wingate:
Directional NMF for joint source localization and separation. WASPAA 2015: 1-5 - [i2]Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis:
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation. CoRR abs/1502.04149 (2015) - [i1]Y. Cem Sübakan, Johannes Traa, Paris Smaragdis, Noah D. Stein:
A Dictionary Learning Approach for Factorial Gaussian Models. CoRR abs/1508.04486 (2015) - 2014
- [j17]Paris Smaragdis, Cédric Févotte, Gautham J. Mysore, Nasser Mohammadiha, Matthew D. Hoffman:
Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view. IEEE Signal Process. Mag. 31(3): 66-75 (2014) - [j16]Johannes Traa, Paris Smaragdis:
Multichannel source separation and tracking with RANSAC and directional statistics. IEEE ACM Trans. Audio Speech Lang. Process. 22(12): 2233-2243 (2014) - [c62]Minje Kim, Paris Smaragdis:
Efficient model selection for speech enhancement using a deflation method for Nonnegative Matrix Factorization. GlobalSIP 2014: 537-541 - [c61]Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis:
Deep learning for monaural speech separation. ICASSP 2014: 1562-1566 - [c60]Johannes Traa, Minje Kim, Paris Smaragdis:
Phase and level difference fusion for robust multichannel source separation. ICASSP 2014: 6687-6691 - [c59]Johannes Traa, Paris Smaragdis:
Robust interchannel phase difference modeling with wrapped regression splines. SAM 2014: 69-72 - [c58]Ding Liu, Paris Smaragdis, Minje Kim:
Experiments on deep learning for speech denoising. INTERSPEECH 2014: 2685-2689 - [c57]Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis:
Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks. ISMIR 2014: 477-482 - [c56]Johannes Traa, Paris Smaragdis:
Multiple speaker tracking with the Factorial von Mises-Fisher Filter. MLSP 2014: 1-6 - [c55]Y. Cem Sübakan, Johannes Traa, Paris Smaragdis:
Spectral Learning of Mixture of Hidden Markov Models. NIPS 2014: 2249-2257 - 2013
- [j15]Johannes Traa, Paris Smaragdis:
A Wrapped Kalman Filter for Azimuthal Speaker Tracking. IEEE Signal Process. Lett. 20(12): 1257-1260 (2013) - [j14]Manas A. Pathak, Bhiksha Raj, Shantanu Rane, Paris Smaragdis:
Privacy-Preserving Speech Processing: Cryptographic and String-Matching Frameworks Show Promise. IEEE Signal Process. Mag. 30(2): 62-74 (2013) - [j13]Nasser Mohammadiha, Paris Smaragdis, Arne Leijon:
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization. IEEE Trans. Speech Audio Process. 21(10): 2140-2151 (2013) - [c54]Nasser Mohammadiha, Paris Smaragdis, Arne Leijon:
Prediction based filtering and smoothing to exploit temporal dependencies in NMF. ICASSP 2013: 873-877 - [c53]Minje Kim, Paris Smaragdis:
Collaborative audio enhancement using probabilistic latent component sharing. ICASSP 2013: 896-900 - [c52]Johannes Traa, Paris Smaragdis:
Blind multi-channel source separation by circular-linear statistical modeling of phase differences. ICASSP 2013: 4320-4324 - [c51]Minje Kim, Paris Smaragdis:
Manifold Preserving Hierarchical Topic Models for Quantization and Approximation. ICML (3) 2013: 1373-1381 - [c50]Chuanjun Zhang, Glenn G. Ko, Jungwook Choi, Shang-nien Tsai, Minje Kim, Abner Guzmán-Rivera, Rob A. Rutenbar, Paris Smaragdis, Mi Sun Park, Vijaykrishnan Narayanan, Hongyi Xin, Onur Mutlu, Bin Li, Li Zhao, Mei Chen:
EMERALD: Characterization of emerging applications and algorithms for low-power devices. ISPASS 2013: 122-123 - [c49]Minje Kim, Paris Smaragdis:
Single channel source separation using smooth Nonnegative Matrix Factorization with Markov Random Fields. MLSP 2013: 1-6 - [c48]Nasser Mohammadiha, Paris Smaragdis, Arne Leijon:
Simultaneous noise classification and reduction using a priori learned models. MLSP 2013: 1-6 - [c47]Nasser Mohammadiha, Paris Smaragdis, Arne Leijon:
Low-artifact source separation using probabilistic latent component analysis. WASPAA 2013: 1-4 - [c46]Paris Smaragdis:
About this non-negative business. WASPAA 2013: 1 - [c45]Paris Smaragdis, Minje Kim:
Non-negative matrix factorization for irregularly-spaced transforms. WASPAA 2013: 1-4 - [c44]DeLiang Wang, Rainer Martin, Peter Vary, Paris Smaragdis:
Keynote addresses: From auditory masking to binary classification: Machine learning for speech separation. WASPAA 2013: 1-3 - 2012
- [j12]