Claudia Pelletier

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The Cepstral analysis is proposed with Gaussian Mixture Models (GMM) method to classify respiratory sounds in two categories: normal and wheezing. The sound signal is divided in overlapped segments, which are characterized by a reduced dimension feature vectors using Mel-Frequency Cepstral Coefficients (MFCC) or subband based Cepstral parameters (SBC). The(More)
The Gaussian mixture models (GMM) method is proposed to classify respiratory sounds in two categories: normal and wheezing. The sound signal is divided in overlapped segments, which are characterized by reduced dimension feature vectors using cepstral or wavelet transforms. The proposed method is compared with other classifiers: vector quantization (VQ) and(More)
In an interorganizational relationships (IOR) context, interorganizational information systems (IOS) need to be integrated in order to support collaboration between partners and provide a fuller exploitation of the systems they share. Although research stresses the importance of the two phases of the IOS integration, that is the systems development and the(More)
In this paper we describe a methodology that emerged during an implementation of a health-and-social-care-oriented data repository, which consists in grouping information from heterogeneous and distributed information sources. We developed this methodology by first constructing a concrete data repository, containing information about elderly patients flows(More)
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