| 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 | 457 | 2015 |
| Deep learning for monaural speech separation PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis IEEE International Conference on Acoustics, Speech, and Signal Processing …, 2014 | 448 | 2014 |
| Bitwise Neural Networks M Kim, P Smaragdis International Conference on Machine Learning (ICML) Workshop on Resource …, 2015 | 265 | 2015 |
| SINGING-VOICE SEPARATION FROM MONAURAL RECORDINGS USING DEEP RECURRENT NEURAL NETWORKS PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis International Society for Music Information Retrieval Conference, 2014 | 159 | 2014 |
| Experiments on Deep Learning for Speech Denoising D Liu, P Smaragdis, M Kim Annual Conference of the International Speech Communication Association …, 2014 | 119 | 2014 |
| DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car MG Bechtel, E McEllhiney, M Kim, H Yun IEEE International Conference on Embedded and Real-Time Computing Systems …, 2018 | 93 | 2018 |
| Nonnegative matrix partial co-factorization for drum source separation J Yoo, M Kim, K Kang, S Choi 2010 IEEE International Conference on Acoustics, Speech and Signal …, 2010 | 71 | 2010 |
| XNOR-pop: A processing-in-memory architecture for binary convolutional neural networks in wide-io2 drams L Jiang, M Kim, W Wen, D Wang 2017 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2017 | 59 | 2017 |
| Nonnegative Matrix Partial Co-Factorization for Spectral and Temporal Drum Source Separation M Kim, J Yoo, K Kang, S Choi Selected Topics in Signal Processing, IEEE Journal of 5 (6), 1192-1204, 2011 | 59 | 2011 |
| AutoQ: Automated Kernel-Wise Neural Network Quantization Q Lou, F Guo, L Liu, M Kim, L Jiang International Conference on Learning Representations, 2019 | 48* | 2019 |
| Mixtures of Local Dictionaries for Unsupervised Speech Enhancement M Kim, P Smaragdis IEEE Signal Processing Letters 22 (3), 288 - 292, 2015 | 45 | 2015 |
| Learning everywhere: Pervasive machine learning for effective high-performance computation G Fox, JA Glazier, JCS Kadupitiya, V Jadhao, M Kim, J Qiu, JP Sluka, ... 2019 IEEE International Parallel and Distributed Processing Symposium …, 2019 | 43 | 2019 |
| Adaptive Denoising Autoencoders: A Fine-tuning Scheme to Learn from Test Mixtures M Kim, P Smaragdis International Conference on Latent Variable Analysis and Signal Separation, 2015 | 38 | 2015 |
| Monaural music source separation: Nonnegativity, sparseness, and shift-invariance M Kim, S Choi International Conference on Independent Component Analysis and Signal …, 2006 | 37 | 2006 |
| Integrated voice/audio encoding/decoding device and method whereby the overlap region of a window is adjusted based on the transition interval MJ Kim, SK Beack, TJ Lee, KO Kang, SEO Jeongil, JW Kim, JW Hong, ... US Patent App. 13/502,025, 2012 | 26 | 2012 |
| COLLABORATIVE DEEP LEARNING FOR SPEECH ENHANCEMENT: A RUN-TIME MODEL SELECTION METHOD USING AUTOENCODERS M Kim IEEE International Conference on Acoustics, Speech and Signal Processing …, 2017 | 25 | 2017 |
| Single Channel Source Separation Using Smooth Nonnegative Matrix Factorization with Markov Random Fields M Kim, P Smaragdis IEEE International Workshop on Machine Learning for Signal Processing, 2013 | 25 | 2013 |
| Apparatus for encoding and decoding of integrated speech and audio TJ Lee, SK Baek, MJ Kim, DY Jang, SEO Jeongil, K Kang, JW Hong, ... US Patent 8,903,720, 2014 | 24 | 2014 |
| LPC residual signal encoding/decoding apparatus of modified discrete cosine transform (MDCT)-based unified voice/audio encoding device SK Beack, TJ Lee, MJ Kim, K Kang, DY Jang, JW Hong, SEO Jeongil, ... US Patent 8,898,059, 2014 | 24 | 2014 |
| Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding K Zhen, J Sung, MS Lee, S Beack, M Kim Interspeech 2019, 2019 | 22 | 2019 |
Paris SmaragdisUniversity of Illinois at Urbana-Champaign / Adobe Researchillinois.edu의 이메일 확인됨
