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This article presents a method that uses Linear Prediction Coefficients (LPC) and Mel-Frequency Cepstral Coefficients (MFCC) as features to classify normal and abnormal cardiac sounds. Three different feature vectors were tested: LPC-only, MFCC-only and LPC + MFCC. Different experiments were made with three classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forests, using...
The work presented here proposes a new voice conversion (VC) approach based on hidden Markov models (HMMs) for spectral conversion and excitation estimation. This paper is divided in two main parts: First, an initial HMM-based VC system is presented and compared to a state-of-the-art ML-GMM VC system in a monolingual conversion scenario with parallel training data; The second part shows the necessary...
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