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Quality Evaluation of Speech Transmission via Two-way BPL-PLC Voice Communication System in an Underground Mine

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In order to design a stable and reliable voice communication system, it is essential to know how many resources are necessary for conveying quality content. These parameters may include objective quality of service (QoS) metrics, such as: available bandwidth, bit error rate (BER), delay, latency as well as subjective quality of experience (QoE) related to user expectations. QoE is expressed as clarity of speech and the ability to interpret voice commands with adequate mean opinion score (MOS) grades. This paper describes a quality evaluation study of a two-way speech transmission system via bandwidth over power line – power line communication (BPL-PLC) technology in an operating underground mine. We investigate how different features of the available wired medium can affect end-user quality. The results of the described study include: two types of coupling (capacitive and inductive), two transmission modes (mode 1 and 11), and four language sets of speech samples (American English, British English, German, and Polish) encoded at three different bit rates (8, 16, and 24 kbps). Our findings can aid both researchers working on low-bit rate coding and compression, signal processing and speech perception, as well as professionals active in the mining and oil industry.
Rocznik
Strony
585--592
Opis fizyczny
Bibliogr. 36 poz., fot.., rys., tab., wykr.
Twórcy
  • Faculty of Electronics, Telecommunications and Informatics Gdańsk University of Technology Gdańsk, Poland
  • General Tadeusz Kosciuszko Military University of Land Forces Wrocław, Poland
Bibliografia
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  • 4. Debita G. et al. (2020), Subjective and objective quality evaluation study of BPL-PLC wired medium, Elektronika ir Elektrotechnika, 26(3): 13-19, doi: 10.5755/j01.eie.26.3.25794.
  • 5. Debita G., Habrych M., Tomczyk A., Miedziński B., Wandzio J. (2019), Implementing BPL transmission in MV cable network effectively, Elektronika ir Elektrotechnika, 25(1): 59-65, doi: 10.5755/j01.eie.25.1.22737.
  • 6. Delcroix M. et al. (2019), End-to-end SpeakerBeam for single channel target speech recognition, [in:] INTERSPEECH 2019 - 21th Annual Conference of the International Speech Communication Association, pp. 451-455, doi: 10.21437/Interspeech.2019-1856.
  • 7. Ding S.Y., Liu J.L., Yue M.H. (2021), The use of ZigBee wireless communication technology in industrial automation control, Wireless Communications and Mobile Computing, 2021: 8317862, doi: 10.1155/2021/8317862.
  • 8. Dubey H., Sangwan A., Hansen J.H.L. (2019), Toeplitz inverse covariance based robust speaker clustering for naturalistic audio streams, [in:] INTERSPEECH 2019 - 21th Annual Conference of the International Speech Communication Association, pp. 416-420, doi: 10.21437/Interspeech.2019-1102.
  • 9. Falkowski-Gilski P. et al. (2020), Subjective quality evaluation of speech signals transmitted via BPLPLC wired system, [in:] INTERSPEECH 2020 - 22th Annual Conference of the International Speech Communication Association, pp. 4601-4605, doi: 10.21437/Interspeech.2020-1077.
  • 10. Falkowski-Gilski P., Uhl T. (2020), Current trends in consumption of multimedia content using online streaming platforms: A user-centric survey, Computer Science Review, 37(4): 100268, doi: 10.1016/j.cosrev.2020.100268.
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  • 13. Fuchs G., Ashour C., Bäckström T. (2019), Superwideband spectral envelope modeling for speech coding, [in:] INTERSPEECH 2019 - 21th Annual Conference of the International Speech Communication Association, pp. 416-420, doi: 10.21437/Interspeech.2019-1620.
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  • 15. Hao S., Zhang H.Y. (2021), A cross-layered theoretical model of IEEE 1901 power-line communication networks considering retransmission protocols, IEEE Access, 9: 28805-28821, doi: 10.1109/ACCESS.2021.3059246.
  • 16. Held G. (2016), Understanding Broadband Over Power Line, Auerbach Publications.
  • 17. Helmrich C.R., Markovic G., Edler B. (2014), Improved low-delay MDCT-based coding of both stationary and transient audio signals, [in:] ICASSP 2014 - IEEE International Conference on Acoustic, Speech and Signal Processing, pp. 6954-6958, doi: 10.1109/ICASSP.2014.6854948.
  • 18. Hoßfeld T. et al. (2014), Best practices for QoE crowdtesting: QoE assessment with crowdsourcing, IEEE Transactions on Multimedia, 16(2): 541-558, doi: 10.1109/TMM.2013.2291663.
  • 19. International Telecommunication Union [ITU] (2003), General methods for the subjective assessment of sound quality, ITU Recommendation BS.1284, https://www.itu.int/rec/R-REC-BS.1284/en, access: 21.06.2023.
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  • 23. Kostek B. (2019), Music information retrieval - The impact of technology, crowdsourcing, big data, and the cloud in art, Journal of the Acoustical Society of America, 146(4): 2946, doi: 10.1121/1.5137234.
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  • 28. Meng Z., Gaur Y., Li J., Gong Y. (2019), Speaker adaptation for attention-based end-to-end speech recognition, [in:] INTERSPEECH 2019 - 21th Annual Conference of the International Speech Communication Association, pp. 241-245, doi: 10.21437/Interspeech.2019-3135.
  • 29. Miskiewicz K.,Wojaczek A. (2010), Radio Communication System Using Leaky Feeder in Mines Undergrounds [in Polish: Systemy radiokomunikacji z kablem promieniującym w kopalniach podziemnych] Silesian University of Technology Publishing House, Gliwice.
  • 30. Miskiewicz K., Wojaczek A. (2016), How to assess and improve the quality of voice services in telephone communication and alarm systems in mines, Mining - Informatics, Automation and Electrical Engineering, 2(526): 40-47.
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  • 32. Möller S., Raake A. (2014), Quality of Experience. Advanced Concepts, Applications and Methods, Springer Cham.
  • 33. Pocta P., Beerends J.G. (2015), Subjective and objective assessment of perceived audio quality of current digital audio broadcasting systems and web-casting applications, IEEE Transactions on Broadcasting, 61(3): 407-415, doi: 10.1109/TBC.2015.2424373.
  • 34. Szczodrak M., Czyzewski A., Kotus J., Kostek B. (2014), Frequently updated noise threat maps created with use of supercomputing grid, Noise Mapping, 1(1): 32-39, doi: 10.2478/noise-2014-0004.
  • 35. Une M., Miyazaki R. (2020), Musical-noise-free noise reduction by using biased harmonic regeneration and considering relationship between a priori SNR and sound quality, Applied Acoustics, 168: 107410, doi: 10.1016/j.apacoust.2020.107410.
  • 36. Zamlynska M., Debita G., Falkowski-Gilski P. (2022), Quality analysis of audio-video transmission in an OFDM-based communication system, [in:] Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021, Hara T., Yamaguchi H. [Eds.], pp. 724-736, Springer Cham, doi: 10.1007/978-3-030-94822-1_47.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-11bf6e05-484b-4bcb-8746-f633b37d3fd1
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