Abstract
The goal of this chapter is the classification of the voice transmitted emotions, and in order to achieve it we worked with the Mel cepstrum coefficients for the pre-processing of audio. We also used a Modular Neural Network as the classification method and a new optimization algorithm was implemented: Chemical Reaction Algorithm which hadn’t been used before on the optimization of neural networks to find the architecture of the neural network optimizing the number of layers in each module and the number of neurons by layer. The tests were executed on the Berlin Emotional Speech data base, which was recorded by actors in German language in six different emotional states of which they only considered anger, happiness and sadness.
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Acknowledgments
We would like to express our gratitude to CONACYT, and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.
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Sánchez, C., Melin, P., Astudillo, L. (2014). Chemical Optimization Method for Modular Neural Networks Applied in Emotion Classification. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_26
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DOI: https://doi.org/10.1007/978-3-319-05170-3_26
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