Reference for citation: Semenuk V. V., Skladchikov M. V. Improvement of the human emotional state identification algorithm using MFCC.. Journal of Instrument Engineering. 2024. Vol. 67, N 9. P. 731–740 (in Russian). DOI: 10.17586/0021-34542024-67-9-731-740.

Abstract. 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.

Keywords: neural network, human emotion recognition, convolutional neural network, sound fingerprinting

References:

1. https://habr.com/ru/company/toshibarus/blog/433544/. (in Russ.)
2. Li X., Chen Y., Hu J., Zhang J., & Zhang Z. IEEE Transactions on Affective Computing, 2016, no. 2(7), pp. 149–166.
3. Shiller A.V. Praxema. Journal of Visual Semiotics, 2019, no. 4(22), pp. 223–243. (in Russ.)
4. Ryumina E.V., Karpov A.A. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, no. 2(20), pp. 163–176. (in Russ.)
5. Xu H., Liang X., Sun M., Chen S., & Feng J. IEEE Transactions on Affective Computing, 2018, no. 2(9), pp. 261–274.
6. Zhao M., Adelhardt J., & Kummert F. Proceedings of the 2018 ACM on International Conference on Multimodal Interaction, 2018, pp. 440–447.
7. Shan H., Kan M., Wang S., & Yan S. IEEE Transactions on Affective Computing, 2017, no. 4(8), pp. 512–527.
8. Khrustalev V.O., Zubkov A.V. XXIV regional'naya konferentsiya molodykh uchonykh i issledovateley Volgogradskoy oblasti (XXIV Regional Conference of Young Scientists and Researchers of the Volgograd Region), Volgograd, 2020, рр. 223–224. (in Russ.)
9. Kuritsky V.Yu., Sadov S.V. Komp'yuternyye tekhnologii i analiz dannykh (CTDA'2020) (Computer Technologies and Data Analysis (CTDA'2020)), Proceedings of the II International Scientific and Practical Conference, Minsk, April 23–24, 2020, рр. 245–248. (in Russ.)
10. Khnyunin M.V., Ganelina N.D. Intellektual'nyy potentsial Sibiri (RNSK-2021) (Intellectual Potential of Siberia (RNSC2021)), Collection of scientific papers of the 29th Regional Scientific Student Conference, Novosibirsk, 2021, vol. 3, рр. 179–182. (in Russ.)
11. Artemyeva Zh.G., Krushnaya N.A. Human factor: social psychologist, 2020, no. 1(39), pp. 288–294. (in Russ.)
12. Abadi M.K. & Zeng Z. Proceedings of the INTERSPEECH, 2017, pp. 2362–2366.
13. Semenyuk V.V. Nauchnyye revolyutsii: Sushchnost' i rol' v razvitii nauki i tekhniki (Scientific Revolutions: Essence and Role in the Development of Science and Technology), Scientific and practical conference, Chelyabinsk, 2021, 142 р. (in Russ.)