Audio signal transmission method in network-based audio analytics system
DOI:
https://doi.org/10.30837/ITSSI.2023.26.058Keywords:
audio analytics; audio signal transmission method; traffic management; virtual routes; delayAbstract
The subject matter of the article is аudio signal transmission method in network-based audio analytics system. The creation of a network-based audio analytics system leads to the emergence of new classes of load sources that transmit packetized sound data. Therefore, without constructing adequate mathematical models, it is impossible to build a well-functioning network-based audio analytics system. A fundamental question in traffic theory is the question of load source models. The development of an method for transmitting audio signals in a network-based audio analytics system becomes necessary. Based on this, the goal of the work is to create methods an method for transmitting audio signals in a network-based audio analytics system to ensure efficiency and accuracy in audio analytics. The following tasks were solved in the article: the formation of a model for the system's load sources, investigation of connection and traffic management, implementation of control and traffic monitoring functions in the network, research of methods to ensure the quality of audio signal transmission and the development of a method of transmitting an audio signal by virtual routes switching. To achieve these goals, the following methods are used: mathematical signal processing, data compression algorithms, optimization of network protocols, and the use of high-speed network connections. The obtained results include modeling of the system's load sources, examination of connection and traffic management, investigation of methods to ensure the quality of audio signal transmission and a method of transmitting an audio signal by virtual routes switching was proposed. In conclusion, the possibilities of using simulation modeling of nodes in the network-based audio analytics system are highly limited. This is explained by the fact that the acceptable level of information loss in data centers is very low. The use of the developed method enables effective control and processing of sound information in real-time. This method can find broad applications in various fields, including security, healthcare, management systems, and other industries where the analysis of audio signals is a crucial element.
References
References
Kholiev, V., Barkovska, O. (2023), "Comparative analysis of neural network models for the problem of speaker recognition", Innovative Technologies and Scientific Solutions for Industries, Vol. 24, No. 2, P. 172–178. DOI: 10.30837/ITSSI.2023.24.172.
Barkovska, O., Kholiev, V., Lytvynenko, V. (2022), "Study of noise reduction methods in the sound sequence when solving the speech-to-text problem", Advanced Information Systems, Vol. 6, No. 1, P. 48–54. DOI: 10.20998/2522-9052.2022.1.08.
Mykhailichenko, I., Ivashchenko, H., Barkovska O., Liashenko O. (2022), "Application of Deep Neural Network for Real-Time Voice Command Recognition", 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), P. 1-4. DOI: 10.1109/KhPIWeek57572.2022.9916473.
Kovalenko, A., Poroshenko, A. (2022), "ANALYSIS OF THE SOUND EVENT DETECTION METHODS AND SYSTEMS", Advanced Information Systems, Vol. 6, No. 1, P. 65–69. DOI: 10.20998/2522-9052.2022.1.11.
Kholiev, V., Barkovska, O. (2023), "Analysis of the of training and test data distribution for audio series classification", Інформаційно-керуючі системи на залізничному транспорті, Vol. 28, No. 1, P. 38–43. DOI: 10.18664/ikszt.v28i1.276343.
Barkovska, O. (2022), "Performance study of the text analysis module in the proposed model of automatic speaker’s speech annotation", Computer systems and information technologies, Vol. 4, P. 13–19. DOI: 10.31891/csit-2022-4-2.
Poroshenko, A., Kovalenko, A., Sedlaček, P. (2022), "Organization of Audio Analytics Systems Topologies", 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), P. 517–521, DOI: 10.1109/PICST57299.2022.10238687.
Poroshenko, A., Kovalenko, A. (2023), "Optimization of a basic network in audio analytics systems", Advanced Information Systems, Vol. 7, No. 1, P. 23–28. DOI: 10.20998/2522-9052.2023.1.04.
Kuchuk, N., Kovalenko, A., Kuchuk, H., Levashenko, V., Zaitseva, E. (2022), "Mathematical Methods of Reliability Analysis of the Network Structures: Securing QoS on Hyperconverged Networks for Traffic Anomalies". Future Intent-Based Networking. Lecture Notes in Electrical Engineering, Vol. 831. Springer, Cham. P.223-241. DOI: 10.1007/978-3-030-92435-5_13.
Ghido, F., Tabus, I. (2013), "Sparse Modeling for Lossless Audio Compression", IEEE Transactions on Audio, Speech, and Language Processing, Vol. 21, No. 1, P. 14–28. DOI: 10.1109/TASL.2012.2211014.
Huang, H., Shu, H., Yu, R. (2014), "Lossless audio compression in the new IEEE Standard for Advanced Audio Coding", 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), P. 6934–6938. DOI: 10.1109/ICASSP.2014.6854944.
Zeghidour, N., Luebs, A., Omran, A., Skoglund, J., Tagliasacchi, M. (2022), "SoundStream: An End-to-End Neural Audio Codec". IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 30, P. 495-507. DOI: 10.1109/TASLP.2021.3129994.
Petrosky, E. E., Michaels, A. J., Ridge, D. B. (2019), "Network Scalability Comparison of IEEE 802.15.4 and Receiver-Assigned CDMA", IEEE Internet of Things Journal. Vol. 6, No. 4, P. 6060–6069. DOI: 10.1109/JIOT.2018.2884455.
Meng, X., Pappas, V., Zhang, L. (2010), "Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement", 2010 Proceedings IEEE INFOCOM, P. 1–9. DOI: 10.1109/INFCOM.2010.5461930.
Amarudin, Ferdiana, R., Widyawan (2020), "A Systematic Literature Review of Intrusion Detection System for Network Security: Research Trends, Datasets and Methods", 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), P. 1–6. DOI: 10.1109/ICICoS51170.2020.9299068.
Do, E. H., Gadepally, V. N. (2020), "Classifying Anomalies for Network Security", ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), P. 2907–2911. DOI: 10.1109/ICASSP40776.2020.9053419.
Szymanski, A., Lason, A., Rzasa J., Jajszczyk, A. (2007), "Grade-of-service-based routing in optical networks [Quality-of-Service-Based Routing Algorithms for Heterogeneous Networks]", IEEE Communications Magazine, Vol. 45, No. 2, P. 82-87. DOI: 10.1109/MCOM.2007.313400.
Liu, C. H., Gkelias, A., Leung, K. K. (2008), "Connection admission control and grade of service for QoS routing in mesh networks", 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, P. 1–5. DOI: 10.1109/PIMRC.2008.4699895.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.