IMPROVEMENT OF THE HUMAN EMOTIONAL STATE IDENTIFICATION ALGORITHM USING MFCC
Annotation
An approach to the implementation of an algorithm for the emotional state of a person using convolutional neural networks is presented. Based on the general concept of scientific research, a variant of complicating the hierarchy of identifiable emotions is considered. A comparative analysis of the application of the windowed Fourier transform and the MFCC algorithm as a tool for processing information data is carried out. The variant of complication of the proposed method is considered as a logical transition from a simpler mathematical apparatus, presented in the form of a windowed Fourier transform to the use of mel-frequency cepstral coefficients. This allowed to form a more informative input data set without complicating the neural network architecture, the methodology of scientific research was adjusted and, using an idealized database, the accuracy of identification close to 100% was achieved. The rationale for using Deep Network Designer as a tool for creating neural network architecture is given.
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