OPTIMIZATION OF A BASIC NETWORK IN AUDIO ANALYTICS SYSTEMS
Main Article Content
Relevance. The sound is a source of data that provides information necessary for survival and warns of potential dangers. Audio analytics solutions allows detecting and responding in time to illegal actions and violations of the law, which are accompanied by appropriate sounds. Therefore, the problem of reducing delays in the transmission of audio streams in network-based systems of audio analytics becomes relevant. The object of research is the process of audio signals transmitting. The subject of the research is mathematical models of audio and video streams transmission in network systems. The purpose of this paper is to develop an approximate method for quickly solving optimization equations for a network of connecting links and to assess the adequacy of the developed method. Research results. A method for selecting the network structure of the audio analytics system is proposed. The optimization of the network structure of audio analytics system under non-ordinary Poisson load is presented. The optimization of the basic network of audio analytics system was studied according to the cost criterion. Comparisons of the optimization results shows that the given link distribution options are not significantly different from each other and are close in cost.
Poroshenko, A. (2022), “Mathematical model of the passage of audio signals in network-based audio analytics sys-tems”, Advanced Information Systems, vol. 6, no. 4, pp. 25–29, doi: https://doi.org/10.20998/2522-9052.2022.4.04
Liu, Q. and Wu, J. (2021), “Parameter Tuning-Free Missing-Feature Reconstruction for Robust Sound Recognition”, IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 1, pp. 78-89, doi: https://doi.org/10.1109/JSTSP.2020.3038054
Dong, X., Yin, B., Cong, Y., Du, Z. and Huang, X. (2020), “Environment Sound Event Classification With a Two-Stream Convolutional Neural Network”, IEEE Access, vol. 8, pp. 1257.14-1257.21, doi: https://doi.org/10.1109/ACCESS.2020.3007906
Mesaros, A., Heittola, T., Virtanen, T. and Plumbley, M. D. (2021), “Sound Event Detection: A tutorial”, IEEE Signal Processing Magazine, vol. 38, no. 5, pp. 67-83, doi: https://doi.org/10.1109/MSP.2021.3090678
Kovalenko, A. and Poroshenko, A. (2022), “analysis of the sound event detection methods and systems”, Advanced Information Systems, vol. 6, no. 1, pp. 65-69, doi: https://doi.org/10.20998/2522-9052.2022.1.11
Kong, Q., Xu, Y., Wang, W. and Plumbley, M. D. (2020), “Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization”, IEEE/ACM Transactions on Audio, Speech, and Lan-guage Processing, vol. 28, pp. 2450-2460, doi: https://doi.org/10.1109/TASLP.2020.3014737
Zeghidour, N., Luebs, A., Omran, A., Skoglund, J. and Tagliasacchi, M. (2022), “SoundStream: An End-to-End Neural Audio Codec”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 495-507, doi:
Pinski E. A. (1990), “Simple approximation for the Erlang loss function”, Performance evaluation, No. 5, pp. 131–136.
Pratt, C.W. (1967), “The concept of marginal overflow in alternate routing”, The 5th ITC, New-York, June, pp. 52–58.46.
Bailey, A. (2000), Network Technology for Digital Audio (1st ed.), Routledge, 275 p. doi: