Conversational Scene Analysis
This is the topic of my dissertation: the basic premise is that we can infer various aspects about the state of a conversation without being able to understand the words that are being said. I am using the feature estimation techniques from my earlier work to build statistical models of interactions that can make inferences and predictions about the conversational state. PhD Thesis.


Mania/Depression Assessment
Clinicians have long noted that changes in speaking patterns, particularly in pitch and speaking rate, accompany depression and mania. The goal of this study is to quantify these effects from longtiudinal patient data acquired with a cellphone, in the hopes of developing a quantitative measure of relative depression/mania levels. This is joint work with Vikram Kumar (HST) and Dr. Roy Perlis (Mass. Gen. Hospital), and is part of a clinical study at MGH.


The Influence Model
The Influence Model was developed by Chalee Asavathiratham as a generative mechanism to efficiently model the effects of many interacting Markov processes. Its topology is that of a coupled HMM with N chains, but the CPT's are mixtures of the pairwise CPTs. Exact learning is possible with tables of dim N (vs. 2N - details here); we have developed an approximate algorithm. Joint work with Brian Clarkson, Tanzeem Choudhury. Technical Report.

The Facilitator Room
The facilitator room is a computational framework for behavior modification. The goal is to make reliable measurements of the interactions in the room, and then model the effects of actuators on these measurements, and finally using these to facilitate behavior. Joint work with Brian Clarkson, Tanzeem Choudhury. CVPR workshop paper, poster.

Smart Headphones
Ordinary headphones have the unfortunate property of blocking the user's social interactions. "Smart Headphones" counteract this problem by using our speech detection method to pass through speech sounds to the user. In this way, the user can listen to music but still stay engaged in conversations around him. The speech detection algorithm is joint work with Brian Clarkson. CHI paper, slides.

Speech Detection
Speech detection, or "endpoint detection" as it is called by the speech community, has not been explored thoroughly due to the standard assumption of a headset microphone. We are interested in detecting speech in an open environment, and do so with a novel algorithm using the harmonic structure of vowels. Joint work with Brian Clarkson. ICASSP paper, poster.
Pitch Tracking/Prosodic Feature Estimation
While the estimation of pitch and speaking rate have received much attention in the past decades, little of the work has concentrated on robust performance in the far-field case. We are interested in computing these features for a room or wearable setting, where ideal microphone placement is not possible.
Wearable Phased Arrays
This project came from a desire to determine who was speaking when in a wearable setting. Speaker identification techniques fail due to rapid changes; we thus chose to determine the changes in source direction using a wearable phased array. The flexible geometry of the array made it a challenging task. We introduce a dynamic programming algorithm to find when the speaker changes occur. Joint work with Steve Schwartz. ISWC paper, slides.
Bayes Point Machines
Support vector machines (SVM's) are well known for their robustness against generalization error, supposedly due to their maximum margin strategy. The work of Ralf Herbrich showed that a more Bayesian approach to choosing a solution point could yield even better performance. This paper recounts the assumptions made in developing the Bayes Point Machine (BPM) and empirically examines its performance in various data scenarios. Paper, slides.

Independent Components Analysis
Independent Components Analysis is a technique for separating independent signals s that are mixed together by a mixing matrix M. This paper compares three approaches to ICA (Comon, Amari, Bell and Sejnowski), comments on their assumptions, strengths, and weakness, and finally suggests several extensions to existing algorithms. Paper, slides.

Using Orthogonal Wavelets for Multiscale Template Matching
Wavelets are well-known as an efficient means of representing audio/visual information. This work shows how orthogonal wavelets can be used for very efficient template matching schemes in which computations from coarser scales can be reused for computations at the finer scales. Examples are shown for entire image matching as well as image mosaicing. Paper.
Mesh-Based Function Approximation
When tracking mesh-based models, it is necessary to smooth the underlying image to allow the computation of gradients. This is typically done with a fixed kernel. However, the mesh gives us insight as to what level of detail to preserve at which location. This paper develops an efficient algorithm for recursive function approximation that is appropriate to the mesh. Joint work with Kentaro Toyama (Microsoft Research). Technical report.
Maximum A Posteriori Tracking of Physically-Based 3D Modal Mesh Models
There is often little texture to map to each element of a mesh model (as in deformable templates) - sometimes we must be guided only by the probability of belonging to certain classes (lip/skin in our case). We show how this information can be used to very efficiently ascend to a local maximum a posteriori (MAP) solution for modal models. Joint work with Nuria Oliver. Speech Communications journal paper, poster, web page (with videos).
Training 3D Mesh Models from Finite Element Priors
Naive methods to train a mesh model require tracking all vertex locations in data, which is prohibitive for a detailed mesh. We show how finite element techniques can be used to model the basic properties of the mesh (i.e., stiff and loose regions, etc.), allowing data to be taken at only a few points (17 nodes out of 206) and still effectively train the observed physics of the model. Master's thesis, CVPR workshop paper.
Optical Flow Regularization with a 3D Model (applied to Head Tracking)
This work shows how 3D models can be used to regularize optical flow. In particular, we use a simple 3D model (an ellipsoid) to approximate the head, and use robust estimation techniques to track head motion in 3D using this model for regularization. The results were exceedingly stable, though drift eventually creeps in due to its purely differential nature. Joint work with Irfan Essa. ICPR paper, CA paper, web page (videos). see also ICCV paper.
Vision-Steered Audio for Interactive Environments
High-quality audio input and audio imaging in an open environment is a challenge, especially if we do not want to encumber the user with wireless microphones, etc. In this work, we propose a solution using a phased array of microphones for input and an IIR-based cross filter system for output, both steered using information from computer vision (pfinder). Joint work with Michael Casey, Bill Gardner, and Chris Wren. ImageCom paper, AES paper.
Using Hyperacuity Principles for Image Enhancement
What began as an attempt to justify the use of hyperacuity sensors in scanning technologies resulted in a range of image enhancement techniques for standard scanning mechanisms. We show how hyperacuity principles can be applied to grayscale scans for significant improvements to text/continuous tone appearance/legibility. Joint work with David Biegelsen, Warren Jackson, and David Jared (Xerox PARC). SB Thesis.