Mathematical model of the passage of audio signals in network-based audio analytics systems

Main Article Content

Anton Poroshenko

Abstract

Relevance. Nowadays, more and more audio and video analytics systems work together. But in many cases, it is necessary to quickly transfer the audio stream. Therefore, the task of reducing delays in transmission of audio streams in network-based audio analytics systems becomes relevant. The object of research is the process of audio signal transmission. The subject of research is mathematical models of audio and video streams in network systems. The purpose of this paper is to develop a mathematical model for the passage of audio signals in network-based audio analytics systems. Research results. The statement of the problem of optimizing the network of connecting lines according to the criterion of minimum capital costs is proposed. Optimization equations for a unidirectional three-node basic structure are compiled based on the problem of minimizing capital costs. Optimization equations are obtained for a bidirectional three-node basic structure. Obtained optimization equations are generalized to any structure of a network of connecting lines. The direction of further research is the development of an approximate method for quickly solving optimization equations for a network of connecting lines and assessing the adequacy of the developed model.

Article Details

How to Cite
Poroshenko, A. (2022). Mathematical model of the passage of audio signals in network-based audio analytics systems. Advanced Information Systems, 6(4), 25–29. https://doi.org/10.20998/2522-9052.2022.4.04
Section
Information systems modeling
Author Biography

Anton Poroshenko, Kharkiv National University of Radio Electronics, Kharkiv

postgraduate student at Department of Electronic Computers

References

Alain Dufaux, Laurent Besacier, Michael Ansorge, Fausto Pellandini. Automatic sound detection and recognition for noisy environment, IEEE European Signal Processing Conference EUSIPCO. 2000. P. 1–4.

Phan H., Koch P., Katzberg F., Maass M., Mazur R., McLoughlin I., Mertins, A. What makes audio event detection harder than classification?. Proceedings of the 25th European Signal Processing Conference (EUSIPCO). 2017. P. 2739-2743. DOI: https://doi.org/10.23919/EUSIPCO.2017.8081709.

Sami Ur Rahman, Adnan Khan, Sohail Abbas, Fakhre Alam, Nasir Rashid. Hybrid system for automatic detection of gunshots in indoor environment. Multimedia Tools and Applications. 2021. Vol. 80. P. 4143-4153, doi: https://doi.org/10.1007/s11042-020-09936-w.

Nicolas Turpault, Romain, Serizel. Training Sound Event Detection on a Heterogeneous Dataset. DCASE. 2020. URL: https://arxiv.org/abs/2007.03931.

Salamon J., MacConnell D., Cartwright M., Li,P., Bello, J.P. Scaper: A library for soundscape synthesis and augmentation. 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). P. 344-348, doi: https://doi.org/10.1109/WASPAA.2017.8170052.

Ankit Shah, Anurag Kumar, Alexander G. Hauptmann, Bhiksha Raj. A Closer Look at Weak Label Learning for Audio Events. 2018. arXiv:1804.09288. 10 p. DOI: https://doi.org/10.48550/arXiv.1804.09288.

Порошенко А. І., Коваленко А. А. Методи та підходи до детектування аудіоподій різних типів. Сучасні напрями розвитку інформаційно-комунікаційних технологій та засобів управління: Матеріали одинадцятої міжнародної НТК, Баку: ВА ЗС АР; Харків: НТУ «ХПІ»; Київ: НАУ; Харків: ДП «ПДПРОНДІАВІАПРОМ»; Жиліна: УмЖ, 2021,

-9 квітня 2021. Т.2. С. 114.

Порошенко А. І., Коваленко А. А. Методи класифікації ознак аудіосигналів. Проблеми інформатизації : тези доп. 9-ї міжнар. наук.-техн. конф., 18-19 листопада 2021 р., Черкаси, Харків, Баку, Бельсько-Бяла, [у 3 т.]. Т. 1 / Черк. держ. технолог. ун-т [та ін.]. Харків : Петров В. В., 2021. С. 90.

Kumar K., Chaturvedi K. An Audio Classification Approach using Feature extraction neural network classifica-tion Approach. 2nd International Conference on Data, Engineering and Applications (IDEA). 2020. P. 1-6, doi: https://doi.org/10.1109/IDEA49133.2020.9170702.

Hirata K., Kato,T., Oshima, R. Classification of Environmental Sounds Using Convolutional Neural Network with Bispectral Analysis, 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). 2019.

P. 1-2, DOI: https://doi.org/10.1109/ISPACS48206.2019.8986304.

Pratt C.W. The concept of marginal overflow in alternate routing. The 5th ITC. New-York. June 1967. P 52–58.

Wallstrom A. Methods for Optimizing Alternative Routing Networks, Ericsson Technics, 1969. Nо. 1. P. 3–29.

Tzinis, E., Wisdom, S., Hershey, J.R., Jansen, A. and Ellis, D.P.W. Improving Universal Sound Separation Using Sound Classification. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. P. 96-100. DOI : https://doi.org/10.1109/ICASSP40776.2020.9053921 .

Sose S., Mali S., Mahajan, S.P. Sound Source Separation Using Neural Network. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2019. P. 1-5, DOI:

https://doi.org/10.1109/ICCCNT45670.2019.8944614 .

Zheng X., Chen H., Song Y. Zheng ustc teams submission for dcase2021 task4 semi-supervised sound event detection. DCASE2021 Challenge, Tech. Rep, 2021.