6 papers
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Alexander Lin, Andrew H. Song, Berkin Bilgic +1
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem. However, the most popular inference algorithms for SBL become too expensive for high-…
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Bahareh Tolooshams, Ritwik Giri, Andrew H. Song +2
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary ma…
Fast Convolutional Dictionary Learning off the Grid
Andrew H. Song, Francisco J. Flores, Demba Ba
Given a continuous-time signal that can be modeled as the superposition of localized, time-shifted events from multiple sources, the goal of Convolutional Dictionary Learning (CDL)…
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Bahareh Tolooshams, Andrew H. Song, Simona Temereanca +1
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e.g., count data). The architectures are inspired by the problem of learn…
Spike Sorting by Convolutional Dictionary Learning
Andrew H. Song, Francisco Flores, Demba Ba
Spike sorting refers to the problem of assigning action potentials observed in extra-cellular recordings of neural activity to the neuron(s) from which they originate. We cast this…
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference
Leon Chlon, Andrew Song, Sandya Subramanian +4
Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research. The standard of care is to manually classify 30-second epoch…