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We describe a system that attempts to predict the continuation of a symbolically encoded tabla composition at each time step using a variable-length n-gram model. Using cross-entropy as a measure of model fit, the best model attained an entropy rate of 0.780 in a cross-validation experiment, showing that symbolic tabla compositions can be effectively(More)
We describe a realtime tabla generation system based on a variable-length <i>n</i>-gram model trained on a large symbolic tabla database. A novel, parametric smoothing algorithm based on a family of exponential curves is introduced to control the relative weight of high- and low-order models. This technique is shown to lead to improvements over a back-off(More)
BACKGROUND The Unified Medical Language System (UMLS) contains many important ontologies in which terms are connected by semantic relations. For many studies on the relationships between biomedical concepts, the use of transitively associated information from ontologies and the UMLS has been shown to be effective. Although there are a few tools and methods(More)
Efficiently mining multiple drug interactions and reactions from Adverse Event Reporting System (AERS) is a challenging problem which has not been sufficiently addressed by existing methods. To tackle this challenge, we propose a FCI-fliter approach which leverages the efforts of UMLS mapping, frequent closed itemset mining, and uninformative association(More)
In this paper, we connect local properties in a mobile planar multi-robot team to the task of creating decentralized real time algorithmic music. Using a nonlinear formation control law inspired by the consensus equation, we map the local motion parameters of robots to Euclidean rhythms with the use of sequencers. The control parameters allow a human user(More)
A mixed media tool was created that promotes ensemble virtuosity through tight coordination and interdepence in musical performance. Two different types of performers interact with a virtual space using Wii remote and tangible interfaces using the reacTIVision toolkit [11]. One group of performers uses a tangible tabletop interface to place and move sound(More)
Research in speech emotion recognition often involves features that are extracted in lab settings or scenarios where speech quality is high. However, a great deal of communication occurs through speech codecs, which alters the speech signal and features extracted from it. The purpose of this study is to report on the performance degradation in emotion(More)
Detecting child and adult vocalizations, and computing their characteristics from audio recorded in natural home environments can be useful in many applications. The current study is interested in monitoring children with autism spectrum disorder to ultimately provide outcome measures that can track the efficacy of clinical treatments. In this paper, we(More)