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Emotion Recognition in Sound

Student: Anastasia Popova

Supervisor: Alexander Ponomarenko

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 9

Year of Graduation: 2018

In this paper we consider the automatic emotions recognition problem, especially the case of digital audio signal processing. We consider and verify an approach in which the classification of a sound fragment can be solved using long short-term memory neural network. The computational experiment was done based on Ravdess open dataset including 8 different emotions: "neutral", "calm", "happy," "sad," "angry," "scared", "disgust", "surprised". The best accuracy result was 99%, which was produced by using LSTM network and MFCC.

Full text (added May 16, 2018)

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