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  • Детектирование эмоций в мультимедиа контенте
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Детектирование эмоций в мультимедиа контенте

С. 852–857.
А. С. Попова, А. Г. Рассадин, А. А. Пономаренко

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 is reduced to the problem of image recognition. The waveform and spectrogram are used as a visual representation of the image. The computational experiment was done based on Radvess open dataset including 8 different emotions: "neutral", "calm", "happy," "sad," "angry," "scared", "disgust", "surprised". The best accuracy result was 64%, which was produced by a combination of “|spectrogram + convolution neural network VGG-11”

Language: Russian
Full text
Keywords: classificationspeech recognitionemotion recognitiondeep learningconvolutional neural networksИСТ-2017audio recognition
Publication based on the results of:
Разработка и апробация эффективных методов классификации для больших баз мультимедийных данных (2017)

In book

Материалы XXIII международной научно-технической конференции «Информационные системы и технологии-2017»
[б.и.], 2017.
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