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2020 – today
- 2023
[i33]Darius Petermann, Inseon Jang, Minje Kim:
Native Multi-Band Audio Coding within Hyper-Autoencoded Reconstruction Propagation Networks. CoRR abs/2303.08005 (2023)- 2022
- [j9]Vibhatha Abeykoon
, Geoffrey C. Fox, Minje Kim, Saliya Ekanayake, Supun Kamburugamuve, Kannan Govindarajan, Pulasthi Wickramasinghe, Niranda Perera, Chathura Widanage, Ahmet Uyar, Gurhan Gunduz, Selahatin Akkas:
Stochastic gradient descent-based support vector machines training optimization on Big Data and HPC frameworks. Concurr. Comput. Pract. Exp. 34(8) (2022) - [j8]Aswin Sivaraman, Minje Kim:
Efficient Personalized Speech Enhancement Through Self-Supervised Learning. IEEE J. Sel. Top. Signal Process. 16(6): 1342-1356 (2022) - [j7]Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beack, Minje Kim:
Scalable and Efficient Neural Speech Coding: A Hybrid Design. IEEE ACM Trans. Audio Speech Lang. Process. 30: 12-25 (2022) - [j6]Sunwoo Kim, Minje Kim:
Boosted Locality Sensitive Hashing: Discriminative, Efficient, and Scalable Binary Codes for Source Separation. IEEE ACM Trans. Audio Speech Lang. Process. 30: 2659-2672 (2022) - [c51]Darius Petermann, Minje Kim:
Spain-Net: Spatially-Informed Stereophonic Music Source Separation. ICASSP 2022: 106-110 - [c50]Haici Yang, Shivani Firodiya, Nicholas J. Bryan, Minje Kim:
Don't Separate, Learn To Remix: End-To-End Neural Remixing With Joint Optimization. ICASSP 2022: 116-120 - [c49]Sunwoo Kim, Minje Kim:
Bloom-Net: Blockwise Optimization for Masking Networks Toward Scalable and Efficient Speech Enhancement. ICASSP 2022: 366-370 - [c48]Haici Yang, Sanna Wager, Spencer Russell, Mike Luo, Minje Kim, Wontak Kim:
Upmixing Via Style Transfer: A Variational Autoencoder for Disentangling Spatial Images And Musical Content. ICASSP 2022: 426-430 - [c47]Hao Zhang, Srivatsan Kandadai, Harsha Rao, Minje Kim, Tarun Pruthi, Trausti Kristjansson:
Deep Adaptive Aec: Hybrid of Deep Learning and Adaptive Acoustic Echo Cancellation. ICASSP 2022: 756-760 - [i32]Darius Petermann, Minje Kim:
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation. CoRR abs/2202.07523 (2022) - [i31]Haici Yang, Sanna Wager, Spencer Russell, Mike Luo, Minje Kim, Wontak Kim:
Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content. CoRR abs/2203.12053 (2022) - [i30]Haici Yang, Wootaek Lim, Minje Kim:
Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding. CoRR abs/2211.02506 (2022) - [i29]Anastasia Kuznetsova, Aswin Sivaraman, Minje Kim:
The Potential of Neural Speech Synthesis-based Data Augmentation for Personalized Speech Enhancement. CoRR abs/2211.07493 (2022) - 2021
- [c46]R. David Badger, Minje Kim:
Singular Value Decomposition for Compression of Large-Scale Radio Frequency Signals. EUSIPCO 2021: 1591-1595 - [c45]R. David Badger, Kristopher H. Jung, Minje Kim:
An Open-Sourced Time-Frequency Domain RF Classification Framework. EUSIPCO 2021: 1701-1705 - [c44]Haici Yang, Kai Zhen, Seungkwon Beack, Minje Kim:
Source-Aware Neural Speech Coding for Noisy Speech Compression. ICASSP 2021: 706-710 - [c43]Aswin Sivaraman, Sunwoo Kim, Minje Kim:
Personalized Speech Enhancement Through Self-Supervised Data Augmentation and Purification. Interspeech 2021: 2676-2680 - [c42]Aswin Sivaraman, Minje Kim:
Zero-Shot Personalized Speech Enhancement Through Speaker-Informed Model Selection. WASPAA 2021: 171-175 - [c41]Sunwoo Kim, Minje Kim:
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation. WASPAA 2021: 176-180 - [c40]Darius Petermann, Seungkwon Beack, Minje Kim:
Harp-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding. WASPAA 2021: 316-320 - [i28]Kai Zhen, Mi Suk Lee, Jongmo Sung, Seungkwon Beack, Minje Kim:
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding. CoRR abs/2101.00054 (2021) - [i27]Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beack, Minje Kim:
Scalable and Efficient Neural Speech Coding. CoRR abs/2103.14776 (2021) - [i26]Aswin Sivaraman, Minje Kim:
Self-Supervised Learning for Personalized Speech Enhancement. CoRR abs/2104.02017 (2021) - [i25]Aswin Sivaraman, Sunwoo Kim, Minje Kim:
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification. CoRR abs/2104.02018 (2021) - [i24]Aswin Sivaraman, Minje Kim:
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection. CoRR abs/2105.03542 (2021) - [i23]Sunwoo Kim, Minje Kim:
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation. CoRR abs/2105.03544 (2021) - [i22]Darius Petermann, Seungkwon Beack, Minje Kim:
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding. CoRR abs/2107.10843 (2021) - [i21]Haici Yang, Shivani Firodiya, Nicholas J. Bryan, Minje Kim:
Neural Remixer: Learning to Remix Music with Interactive Control. CoRR abs/2107.13634 (2021) - [i20]Sunwoo Kim, Minje Kim:
BLOOM-Net: Blockwise Optimization for Masking Networks Toward Scalable and Efficient Speech Enhancement. CoRR abs/2111.09372 (2021) - 2020
- [j5]Kai Zhen, Mi Suk Lee, Jongmo Sung, Seungkwon Beack, Minje Kim:
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding. IEEE Signal Process. Lett. 27: 2159-2163 (2020) - [c39]Sunwoo Kim, Haici Yang, Minje Kim:
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation. ICASSP 2020: 106-110 - [c38]Sanna Wager, George Tzanetakis, Cheng-i Wang, Minje Kim:
Deep Autotuner: A Pitch Correcting Network for Singing Performances. ICASSP 2020: 246-250 - [c37]Kai Zhen, Mi Suk Lee, Jongmo Sung, Seungkwon Beack, Minje Kim:
Efficient and Scalable Neural Residual Waveform Coding with Collaborative Quantization. ICASSP 2020: 361-365 - [c36]Kai Zhen, Mi Suk Lee, Minje Kim:
A Dual-Staged Context Aggregation Method towards Efficient End-to-End Speech Enhancement. ICASSP 2020: 366-370 - [c35]Qian Lou, Feng Guo, Minje Kim, Lantao Liu, Lei Jiang:
AutoQ: Automated Kernel-Wise Neural Network Quantization. ICLR 2020 - [c34]Aswin Sivaraman, Minje Kim:
Sparse Mixture of Local Experts for Efficient Speech Enhancement. INTERSPEECH 2020: 4526-4530 - [i19]Sanna Wager, George Tzanetakis, Cheng-i Wang, Minje Kim:
Deep Autotuner: a Pitch Correcting Network for Singing Performances. CoRR abs/2002.05511 (2020) - [i18]Kai Zhen, Mi Suk Lee, Jongmo Sung, Seungkwon Beack, Minje Kim:
Efficient And Scalable Neural Residual Waveform Coding With Collaborative Quantization. CoRR abs/2002.05604 (2020) - [i17]Sunwoo Kim, Haici Yang, Minje Kim:
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation. CoRR abs/2002.06239 (2020) - [i16]Aswin Sivaraman, Minje Kim:
Sparse Mixture of Local Experts for Efficient Speech Enhancement. CoRR abs/2005.08128 (2020) - [i15]Aswin Sivaraman, Minje Kim:
Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement. CoRR abs/2011.03426 (2020)
2010 – 2019
- 2019
- [c33]Vibhatha Lakmal Abeykoon, Geoffrey Charles Fox, Minje Kim:
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM. CCGRID 2019: 508-517 - [c32]Sunwoo Kim, Mrinmoy Maity, Minje Kim:
Incremental Binarization on Recurrent Neural Networks for Single-channel Source Separation. ICASSP 2019: 376-380 - [c31]Sanna Wager, George Tzanetakis, Stefan Sullivan, Cheng-i Wang, John Shimmin, Minje Kim, Perry Cook:
Intonation: A Dataset of Quality Vocal Performances Refined by Spectral Clustering on Pitch Congruence. ICASSP 2019: 476-480 - [c30]Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beack, Minje Kim:
Cascaded Cross-Module Residual Learning Towards Lightweight End-to-End Speech Coding. INTERSPEECH 2019: 3396-3400 - [c29]Geoffrey C. Fox, James A. Glazier, J. C. S. Kadupitiya, Vikram Jadhao, Minje Kim, Judy Qiu, James P. Sluka, Endre T. Somogyi, Madhav V. Marathe, Abhijin Adiga, Jiangzhuo Chen, Oliver Beckstein, Shantenu Jha:
Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation. IPDPS Workshops 2019: 422-429 - [i14]Sanna Wager, George Tzanetakis, Cheng-i Wang, Lijiang Guo, Aswin Sivaraman, Minje Kim:
Deep Autotuner: A Data-Driven Approach to Natural-Sounding Pitch Correction for Singing Voice in Karaoke Performances. CoRR abs/1902.00956 (2019) - [i13]Qian Lou, Lantao Liu, Minje Kim, Lei Jiang:
AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices. CoRR abs/1902.05690 (2019) - [i12]Geoffrey C. Fox, James A. Glazier, J. C. S. Kadupitiya, Vikram Jadhao, Minje Kim, Judy Qiu, James P. Sluka, Endre T. Somogyi, Madhav V. Marathe, Abhijin Adiga, Jiangzhuo Chen, Oliver Beckstein, Shantenu Jha:
Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation. CoRR abs/1902.10810 (2019) - [i11]Vibhatha Abeykoon, Geoffrey C. Fox, Minje Kim:
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM. CoRR abs/1905.01219 (2019) - [i10]Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beack, Minje Kim:
Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding. CoRR abs/1906.07769 (2019) - [i9]Kai Zhen, Mi Suk Lee, Minje Kim:
Efficient Context Aggregation for End-to-End Speech Enhancement Using a Densely Connected Convolutional and Recurrent Network. CoRR abs/1908.06468 (2019) - [i8]Sunwoo Kim, Mrinmoy Maity, Minje Kim:
Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation. CoRR abs/1908.08898 (2019) - [i7]Sunwoo Kim, Minje Kim:
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing. CoRR abs/1908.09799 (2019) - 2018
- [c28]Matthew Setzler, Tyler Marghetis, Minje Kim:
Creative leaps in musical ecosystems: early warning signals of critical transitions in professional jazz. CogSci 2018 - [c27]Sanna Wager, Minje Kim:
Collaborative Speech Dereverberation: Regularized Tensor Factorization for Crowdsourced Multi-Channel Recordings. EUSIPCO 2018: 1532-1536 - [c26]Minje Kim, Paris Smaragdis:
Bitwise Neural Networks for Efficient Single-Channel Source Separation. ICASSP 2018: 701-705 - [c25]Lijiang Guo, Minje Kim:
Bitwise Source Separation on Hashed Spectra: An Efficient Posterior Estimation Scheme Using Partial Rank Order Metrics. ICASSP 2018: 761-765 - [c24]Michael Garrett Bechtel, Elise McEllhiney, Minje Kim, Heechul Yun:
DeepPicar: A Low-Cost Deep Neural Network-Based Autonomous Car. RTCSA 2018: 11-21 - [i6]Kai Zhen, Aswin Sivaraman, Jongmo Sung, Minje Kim:
On Psychoacoustically Weighted Cost Functions Towards Resource-Efficient Deep Neural Networks for Speech Denoising. CoRR abs/1801.09774 (2018) - [i5]Sanna Wager, Lijiang Guo, Aswin Sivaraman, Minje Kim:
A Data-Driven Approach to Smooth Pitch Correction for Singing Voice in Pop Music. CoRR abs/1805.02603 (2018) - 2017
- [j4]Hongwei Wang, Yunlong Gao, Shaohan Hu, Shiguang Wang, Renato Mancuso, Minje Kim, Po-Liang Wu, Lu Su, Lui Sha, Tarek F. Abdelzaher:
On Exploiting Structured Human Interactions to Enhance Sensing Accuracy in Cyber-physical Systems. ACM Trans. Cyber Phys. Syst. 1(3): 16:1-16:19 (2017) - [c23]Minje Kim:
Collaborative Deep Learning for speech enhancement: A run-time model selection method using autoencoders. ICASSP 2017: 76-80 - [c22]Sanna Wager, Liang Chen, Minje Kim, Christopher Raphael:
Towards expressive instrument synthesis through smooth frame-by-frame reconstruction: From string to woodwind. ICASSP 2017: 391-395 - [c21]Lei Jiang, Minje Kim, Wujie Wen, Danghui Wang:
XNOR-POP: A processing-in-memory architecture for binary Convolutional Neural Networks in Wide-IO2 DRAMs. ISLPED 2017: 1-6 - [i4]Minje Kim:
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders. CoRR abs/1705.10385 (2017) - [i3]Lijiang Guo, Minje Kim:
Bitwise Source Separation on Hashed Spectra: An Efficient Posterior Estimation Scheme Using Partial Rank Order Metrics. CoRR abs/1708.06750 (2017) - 2016
- [c20]Minje Kim, Paris Smaragdis:
Efficient neighborhood-based topic modeling for collaborative audio enhancement on massive crowdsourced recordings. ICASSP 2016: 41-45 - [i2]Minje Kim, Paris Smaragdis:
Bitwise Neural Networks. CoRR abs/1601.06071 (2016) - 2015
- [j3]Minje Kim, Paris Smaragdis:
Mixtures of Local Dictionaries for Unsupervised Speech Enhancement. IEEE Signal Process. Lett. 22(3): 288-292 (2015) - [j2]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) - [c19]Minje Kim, Paris Smaragdis:
Adaptive Denoising Autoencoders: A Fine-Tuning Scheme to Learn from Test Mixtures. LVA/ICA 2015: 100-107 - [c18]Minje Kim, Paris Smaragdis, Gautham J. Mysore:
Efficient manifold preserving audio source separation using locality sensitive hashing. ICASSP 2015: 479-483 - [c17]Yunlong Gao, Shaohan Hu, Renato Mancuso, Hongwei Wang, Minje Kim, Po-Liang Wu, Lu Su, Lui Sha, Tarek F. Abdelzaher:
Exploiting structured human interactions to enhance estimation accuracy in cyber-physical systems. ICCPS 2015: 60-69 - [i1]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) - 2014
- [c16]Minje Kim, Paris Smaragdis:
Efficient model selection for speech enhancement using a deflation method for Nonnegative Matrix Factorization. GlobalSIP 2014: 537-541 - [c15]Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis:
Deep learning for monaural speech separation. ICASSP 2014: 1562-1566 - [c14]Johannes Traa, Minje Kim, Paris Smaragdis:
Phase and level difference fusion for robust multichannel source separation. ICASSP 2014: 6687-6691 - [c13]Ding Liu, Paris Smaragdis, Minje Kim:
Experiments on deep learning for speech denoising. INTERSPEECH 2014: 2685-2689 - [c12]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 - 2013
- [c11]Minje Kim, Paris Smaragdis:
Collaborative audio enhancement using probabilistic latent component sharing. ICASSP 2013: 896-900 - [c10]Minje Kim, Paris Smaragdis:
Manifold Preserving Hierarchical Topic Models for Quantization and Approximation. ICML (3) 2013: 1373-1381 - [c9]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 - [c8]Minje Kim, Paris Smaragdis:
Single channel source separation using smooth Nonnegative Matrix Factorization with Markov Random Fields. MLSP 2013: 1-6 - [c7]Paris Smaragdis, Minje Kim:
Non-negative matrix factorization for irregularly-spaced transforms. WASPAA 2013: 1-4 - 2012
- [c6]Minje Kim, Paris Smaragdis, Glenn G. Ko, Rob A. Rutenbar:
Stereophonic spectrogram segmentation using Markov random fields. MLSP 2012: 1-6 - 2011
- [j1]Minje Kim, Jiho Yoo, Kyeongok Kang, Seungjin Choi:
Nonnegative Matrix Partial Co-Factorization for Spectral and Temporal Drum Source Separation. IEEE J. Sel. Top. Signal Process. 5(6): 1192-1204 (2011) - 2010
- [c5]Jiho Yoo, Minje Kim, Kyeongok Kang, Seungjin Choi:
Nonnegative matrix partial co-factorization for drum source separation. ICASSP 2010: 1942-1945 - [c4]Minje Kim, Jiho Yoo, Kyeongok Kang, Seungjin Choi:
Blind rhythmic source separation: Nonnegativity and repeatability. ICASSP 2010: 2006-2009
2000 – 2009
- 2006
- [c3]Minje Kim, Seungjin Choi:
Monaural Music Source Separation: Nonnegativity, Sparseness, and Shift-Invariance. ICA 2006: 617-624 - [c2]Minje Kim, Seungjin Choi:
ICA-Based Clustering for Resolving Permutation Ambiguity in Frequency-Domain Convolutive Source Separation. ICPR (1) 2006: 950-954 - 2005
- [c1]Minje Kim, Seungjin Choi:
On Spectral Basis Selection for Single Channel Polyphonic Music Separation. ICANN (2) 2005: 157-162
Coauthor Index
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