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This work investigates the effectiveness of features from the spectral envelope such as the frequency and bandwidth of the first peak obtained from a 30(th) order Linear Predictive Coefficients (LPC) to identify pathological voices. Other spectral features are also investigated and tested to improve the recognition rate. The value of the Relative Power of(More)
— First experimental results of the imaging system Clear-PEM for positron emission mammography, under development within the framework of the Crystal Clear Collaboration at CERN, are presented. The quality control procedures of crystal pixels, APD arrays and assembled detector modules are described. The detector module performance was characterized in(More)
OBJECTIVES Speech signal processing techniques have provided several contributions to pathologic voice identification, in which healthy and unhealthy voice samples are evaluated. A less common approach is to identify laryngeal pathologies, for which the use of a noninvasive method for pathologic voice identification is an important step forward for(More)
Voice pathology identification using speech processing methods can be used as a preliminary diagnosis. This study implements a set of identification systems to screen voice pathologies using voice signal features from the sustained vowel /a/ and continuous speech. The two signals tasks are evaluated using three acoustic features applied to four classifiers.(More)
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