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Andreas Spanias
Researcher at Arizona State University
Publications - 512
Citations - 8918
Andreas Spanias is an academic researcher from Arizona State University. The author has contributed to research in topics: Speech coding & Speech processing. The author has an hindex of 36, co-authored 490 publications receiving 7895 citations. Previous affiliations of Andreas Spanias include Arizona's Public Universities & Intel.
Papers
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Journal ArticleDOI
Energy-Efficient Distributed Estimation by Utilizing a Nonlinear Amplifier
TL;DR: It is shown that using an energy-efficient amplify-and-forward distributed estimation scheme has two benefits over linear amplifier operation: improved transmitter efficiency by operating the amplifier in compression, and reduced sensitivity to heavy-tailed distributions due to the soft saturation.
Proceedings ArticleDOI
Location Based Distributed Spectral Clustering for Wireless Sensor Networks
Gowtham Muniraju,Sai Zhang,Cihan Tepedelenlioglu,Mahesh K. Banavar,Andreas Spanias,Cesar Vargas-Rosales,Rafaela Villalpando-Hernandez +6 more
TL;DR: A robust distributed clustering method without a fusion center to group sensors based on their location in a wireless sensor network (WSN) is proposed, which works for any connected graph structure.
Proceedings ArticleDOI
HMM-based speech enhancement using harmonic modeling
TL;DR: The hidden Markov model (HMM) based minimum mean square error (MMSE) estimator is extended to incorporate a ternary voicing state, and applies it to a harmonic representation of voiced speech, and noise reduction during voiced sounds is improved.
Posted Content
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
TL;DR: In this article, a self-attention mechanism is employed for clinical time-series modeling, which employs a masked, self attention mechanism, and uses positional encoding and dense interpolation strategies for incorporating temporal order.
Proceedings ArticleDOI
Learning dictionaries for local sparse coding in image classification
TL;DR: Simulation results demonstrate that the sparse codes computed using the proposed dictionary achieve improved classification accuracies when compared to using a K-means dictionary with standard image datasets.