In this contribution three examples of techniques that can be used for state order estimation of hidden Markov models are given. The methods are also exem-pliied using real laser range data, and the computational burden of the three methods is discussed. Two techniques, Maximum Description Length and Maximum a Posteriori Estimate, are shown to be very similar under certain circumstances. The th...

SUMMARY This paper describes a new presentation of continuous speech in terms of the probability of all phoneme types as a function of time. The presentation is called a phoneme probability presentation (PPP) and can be used for phoneme recognition of continuous speech. As a technique ,,0 produce the PPP, we have employed hidden Markov models (HMM) with time duration information. This informati...

We present a system for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are modeled using hidden Markov models (HMM) and separated using factorial HMM methods. Without prior speaker models for the sources in the mixture it is difficult to exactly resolve the individual sources because there is n...

The segmentation of ECG signal is a useful tool for the diagnosis of cardiac diseases. However, the state-of-the-art methods use hidden Markov models which do not adequately model the transitions between successive waves. This paper uses two methods which attempt to overcome this limitation: a HMM state scission scheme which prevents ingoing and outgoing transitions in the middle of the waves a...

This paper deals with a tool which may help maintenance manager to schedule maintenance activities. To help him, we show that by using events which can be observed on a process, like maintenance events, we can predict failures before they occur. Principles are based on the hypothesis that failure is preceded by a typical sequence of events. We also show that Hidden Markov Models can be used acc...

Hidden Markov Model Regression (HMMR) is an extension of the Hidden Markov Model (HMM) to regression analysis. We assume that the parameters of the regression model are determined by the outcome of a nite-state Markov chain and that the error terms are conditionally independent normally distributed with mean zero and state dependent variance. The theory of HMM regression is quite new, but some ...

A hidden Markov model (HMM) defines a joint probability distribution of a series of observations xt and hidden states zt for t = 1, . . . , T . We use x1:T and z1:T to refer to the full sequence of observations and states respectively. In a HMM the prior on the state sequence is assumed to satisfy the Markov property, which is to say that the probability of each state zt depends only on the pre...

This paper presents an approach to building plans using partially observable Markov decision processes. The approach begins with a base solution that assumes full observability. The partially observable solution is incrementally constructed by considering increasing amounts of information from observations. The base solution directs the expansion of the plan by providing an evaluation function ...

The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states (Beal et al., 2002; Teh et al., 2006). We present a generalization of this framework that introduces nearly block-diagonal structure in the transitions between the hidden states, where blocks correspond to “subbehaviors” exhibited by data sequences. In identifying such struc...

We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 [53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of t...