Paris is a part of the faculty at the University of Illinois and is a part-time research scientist at Adobe. His broader area of interest encompasses anything that involves sound. He spent most of his time working on novel uses of machine learning and signal processing on audio signals, and thinking on what it takes to make a machine listen, understand and appreciate the expressiveness of sound the same way we do.
He completed his Ph.D. and S.M. degrees and did a postdoc at the MIT Media Lab while working at the Machine Listening Group under professor Barry Vercoe. He was also very fortunate to spend time working at some amazing research labs such as Interval Research, Starlab, and Mitsubishi Electric Research Laboratories (MERL). In 2006 he was honored by the MIT Technology Review which recognized him as one of the year’s top 35 young technology innovators.
Collaborative Audio Enhancement Using Probabilistic Latent Component Sharing
Prediction Based Filtering and Smoothing to Exploit Temporal Dependencies in NMF
Clustering and Synchronizing Multi-camera Video via Landmark Cross-correlation
Following Musical Sources by Example
Noise-Robust Dynamic Time Warping Using PLCA Features
A Non-negative Approach to Language Informed Speech Separation
Online PLCA for Real-Time Semi-supervised Source Separation
Sound Recognition in Mixtures
A Convolutive Spectral Decomposition Approach to the Separation of Feedback from Target Speech
A Non-Negative Approach to Semi-Supervised Separation of Speech from Noise with the Use of Temporal Dynamics
Nearest Subspace Representations for Sound Mixtures
Non-negative Hidden Markov Modeling of Audio with Application to Source Separation
Missing Data Imputation for Time-Frequency Representations of Audio Signals
The Markov Selection Model For Concurrent Speech Recognition
Automatic Identification of Individual Killer Whales
A Sparse Non-parametric Approach for Single Channel Separation of Known Sounds
User Guided Audio Selection from Complex Sound Mixtures
Separation by Humming: User Guided Sound Extraction from Monophonic Mixtures
Dynamic Range Extension Using Interleaved Gains
Multipitch Estimation using Sparse Impulse Distributions and Instrument Specific Priors
Hidden Markov and Gaussian Mixture Models for Automatic Call Classification
Relative Pitch Tracking of Multiple Arbitrary Sounds
Probabilistic Factorization of Non-Negative Data with Entropic Co-occurrence Constraints
Sparse and Shift-invariant Feature Extraction from Non- negative Data
Sparse Overcomplete Decomposition for Single Channel speaker Separation
Example-driven Bandwidth Expansion
Sparse Overcomplete Latent Variable Decoposition of Counts Data
A Framework for Secure Speech Recognition
Position and Trajectory Learning for Microphone Arrays
Convolutive Speech Bases and Their Application to Speech Separation
A probabilistic latent variable model for acoustic modeling