Aswin Sivaraman
Title
Cited by
Cited by
Year
On psychoacoustically weighted cost functions towards resource-efficient deep neural networks for speech denoising
K Zhen, A Sivaraman, J Sung, M Kim
arXiv preprint arXiv:1801.09774, 2018
52018
Sparse Mixture of Local Experts for Efficient Speech Enhancement
A Sivaraman, M Kim
Proc. Interspeech 2020, 4526-4530, 2020
42020
Detecting extraneous content in podcasts
S Reddy, Y Yu, A Pappu, A Sivaraman, R Rezapour, R Jones
arXiv preprint arXiv:2103.02585, 2021
22021
Deep Autotuner: A Data-Driven Approach To Natural-Sounding Pitch Correction For Singing Voice In Karaoke Performances
S Wager, G Tzanetakis, C Wang, L Guo, A Sivaraman, M Kim
IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Submitted …, 0
1*
Self-Supervised Learning for Personalized Speech Enhancement
A Sivaraman, M Kim
arXiv preprint arXiv:2104.02017, 2021
2021
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
A Sivaraman, S Kim, M Kim
arXiv preprint arXiv:2104.02018, 2021
2021
Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement
A Sivaraman, M Kim
arXiv preprint arXiv:2011.03426, 2020
2020
Audio signal encoding method and apparatus and audio signal decoding method and apparatus using psychoacoustic-based weighted error function
J Sung, M Kim, A Sivaraman, K Zhen
US Patent App. 16/122,708, 2019
2019
A Data-Driven Approach to Smooth Pitch Correction for Singing Voice in Pop Music
S Wager, L Guo, A Sivaraman, M Kim
arXiv preprint arXiv:1805.02603, 2018
2018
Quantization Error Tolerance in Hashed Audio Spectra
A Sivaraman
2015
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