Structural health monitoring based on guided wave plays an important role in the damage evaluation of practical application. However, the damage evaluation under time-varying environments which introduces undesired uncertainties to guided wave features is difficult to achieve reliably. In this paper, an approach of guided wave based Hidden Markov Model (HMM) method is proposed to improve the re...

We present a system for generating 2D illustrations from hand drawn outlines consisting of only curve strokes. A user can draw a coarse sketch and the system would automatically augment the shape, thickness, color and surrounding texture of the curves making up the sketch. The styles for these refinements are learned from examples whose semantics have been pre-classified. There can be several s...

In this article we propose a modification to the HMRF-EM framework applied to image segmentation. To do so, we introduce a new model for the neighborhood energy function of the Hidden Markov Random Fields model based on the Hidden Markov Model formalism. With this new energy model, we aim at (1) avoiding the use of a key parameter chosen empirically on which the results of the current models ar...

New potential risk factors for cardioembolic strokes are being considered in the medical community. The presence of these factors can be determined by reading an electrocradiogram (ECG). Manual ECG analysis can take hours. We propose combining accurate Hidden Markov Model (HMM) techniques with Apache Spark to improve the speed of ECG analysis. The potential exists for developing a fast classife...

An effective multi-pitch tracking algorithm for noisy speech is critical for auditory processing. However, the performance of existing algorithms is not satisfactory. We have developed a robust algorithm for multi-pitch tracking of noisy speech based on statistical anticipation. By combining an improved channel and peak selection method, a new integration method for extracting periodicity infor...

In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text an...

In this paper, we present an ongoing work which aims at synthesizing speech-laugh sentences in realtime. To do so, the Hidden Markov Model (HMM)based speech-laugh synthesis system will be used along with the MAGE software library. First results are available online on tcts.fpms.ac.be/~laughter/ laughterWorkshop15.

This paper deals with the transcription of vocal melodies in music recordings. The proposed system relies on two distinct pitch estimators which exploit characteristics of the human singing voice. A Hidden Markov Model (HMM) is used to fuse the pitch estimates and make voicing decisions. The resulting performance is evaluated on the MIREX 2006 Audio Melody Extraction data.

There are many shared attributes between existing iterative aligners and Hidden Markov Model (HMM). A learning algorithm of HMM called Viterbi is the same as the iteration of DP-matching of iterative aligners. HMM aligners can use the result of an iterative aligner initially, incorporate the similarity score of amino acids, and apply the detailed gap cost systems to improve the matching accurac...

Hierarchical Hidden Markov Model (HHMM) parsers have been proposed as psycholinguistic models due to their broad coverage within human-like working memory limits (Schuler et al., 2008) and ability to model human reading time behavior according to various complexity metrics (Wu et al., 2010). But HHMMs have been evaluated previously only with very wide beams of several thousand parallel hypothes...