Developing a soprano classifier using FIR-ELM neural network
Abstract: This thesis aims at investigate the feasibility of classifying the soprano singing voice type using a single layer neural network trained with the FIR-ELM algorithm after that the monaural auditory mixture has been segmented with the Harmonic, Percussive and Residual, HPR, decomposition algorithm, previously introduced by Driedger et al.Two different decomposition structures has been evaluated both based on the same HPR decomposition technique. Firstly one single layer that only take advantage of the result of the more pure harmonic and the more pure percussive components of the signal. Secondly, one multilayer structure that further decompose both the harmonic and the percussive components but also takes into account the components that can not be clearly categorized as neither harmonic or percussive components, these are the residual components. The result of the classification was up to 98.5 $\%$ after using these segmentation techniques, this shows that it is feasibly to classify the singing voice type soprano in an monaural source recorded in a non-professional environment using the FIR-ELM algorithm.
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