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个人简介
Paris Smaragdis is best known for contributions in the field of audio processing, more specifically on the problem of source separation (the process of extracting an isolated signal from a mixture). His work on Frequency Domain Independent Component Analysis resulted in the first practical real-time implementations of such systems in the 90s. His later work on Non-Negative Spectral Factorizations was widely adopted for many audio applications, and more recently his introduction of end-to-end deep learning methods for source separation and denoising has resulted in wide adoption.
Smaragdis’ current research focus is on very efficient on-device processing of audio using deep learning, as well as methods for distributed learning over thousands of sensors. He is currently investigating the use of graph models for parameter-free representations of time sequences, binary network signal processing systems, online on-device learning, and is also interested in fully-differentiable digital signal processing systems.
Smaragdis’ current research focus is on very efficient on-device processing of audio using deep learning, as well as methods for distributed learning over thousands of sensors. He is currently investigating the use of graph models for parameter-free representations of time sequences, binary network signal processing systems, online on-device learning, and is also interested in fully-differentiable digital signal processing systems.
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arxiv(2024)
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arxiv(2024)
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CoRR (2024)
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CoRR (2023): 1-5
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CoRR (2023)
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2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)pp.1-6, (2023)
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IEEE Signal Processing Magazineno. 5 (2023): 12-26
Journal of the Acoustical Society of Americano. 3_supplement (2023): A51-A51
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