Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
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Harmonic probabilistic models are common in signal analysis. Framed as a linear-Gaussian state-space model, smoothed inference scales as O(TH2) where H is twice the number of frequencies in the model and T is the length of the time-series. Due to th ...
In this paper we aim to explore what is the most appropriate number of data samples needed when measuring the temporal correspondence between a chosen set of video and audio cues in a given audio-visual sequence. Presently the optimal model that connects s ...
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In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estima ...
A methodology towards person clustering in meeting databases is presented in this report. Such goal is generic to a number of problem in computer vision and more specifically in content-based video indexing and retrieval. First, the audio-stream was consid ...
In this paper, we present a way to track multiple maneuvering targets with varying time-frequency signatures. A particle filter is used to track targets that have constant speeds with changing heading directions. The target motion dynamics help the particl ...
Preliminary results of a new stochastic model for the self-field effect in large Cable In Conduit Conductors (CICC) are presented. We show that the electric properties of CICC can be inferred by using only one (arbitrary and not necessary exact) strand tra ...
We do a case study of two different analysis techniques for studying the stochastic behavior of a randomized system/algorithms: (i) The first approach can be broadly termed as a mean value analysis (MVA), where the evolution of the mean state is studied as ...
In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estima ...
In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...