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Mobile Networks and Applications

11.01.2025 | Research

Intelligent Assessment Method of Communication Interference Speech Quality Based on End-to-end Network

verfasst von: Sen Wang, Jianying Tao, Zheng Dou, Jiangzhi Fu

Erschienen in: Mobile Networks and Applications

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Abstract

Speech quality can reflect the interference in the environment during speech communications. This paper focuses on evaluating speech quality in communication interference environments, and introduces an innovative end-to-end network-based intelligent evaluation method. Utilizing a transformer network structure, the method involves segmenting interference speech into time frames, extracting Mel and amplitude spectrograms, and constructing feature maps for deep feature extraction and quality assessment. Tested on a communication interference speech dataset, this end-to-end approach achieved a remarkable 93% accuracy in evaluating interference speech quality, outperforming CNN-based methods by 5.5%. This significantly enhances the precision of assessing interference speech quality.

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Literatur
1.
Zhang S, Tao Y-r, Zhao Y-x, Chen Y-z (2019) Research on radar jamming evaluation method based on bp neural network. In: 2nd International conference on electrical and electronic engineering (EEE 2019), pp 300–305. Atlantis Press
2.
Yin B, Chen C, Zuo L, Li B, Yuan L, He Y (2021) An evaluation method of electromagnetic interference based on a fast s-transform and time-frequency space model. IEEE Trans Electromagn Compat 64(2):396–404CrossRefMATH
3.
Zhang L, Wang T (2012) A double-ended interference assessing system for short wave transmission. In: 2012 International conference on control engineering and communication technology, pp 220–223. IEEE
4.
Wang S, Lin Y, Hao M, Xu H, Fu J (2023) Assessment of speech communication interference effects under small sample conditions. Wireless Networks, pp 1–15
5.
Wang S, Lin Y, Hao M, Xu H, Tian Q (2022) Interference quality assessment of speech communication based on deep learning. IEEE Trans Reliab 71(2):1011–1021CrossRefMATH
6.
Hao M, Wang S (2021) Evaluation of interference effect of speech communication system. In: 2021 13th International symposium on antennas, propagation and EM theory (ISAPE), pp 1–3. IEEE
7.
Norrenbrock CR, Hinterleitner F, Heute U, Möller S (2015) Quality prediction of synthesized speech based on perceptual quality dimensions. Speech Communi 66:17–35CrossRef
8.
Beerends JG, Hekstra AP, Rix AW, Hollier MP (2002) Perceptual evaluation of speech quality (pesq) the new itu standard for end-to-end speech quality assessment part ii: psychoacoustic model. J Audio Eng Soc 50(10):765–778
9.
Dubey RK, Kumar A (2015) Comparison of subjective and objective speech quality assessment for different degradation/noise conditions. In: 2015 International conference on signal processing and communication (ICSC), pp 261–266. IEEE
10.
Beerends JG, Schmidmer C, Berger J, Obermann M, Ullmann R, Pomy J, Keyhl M (2013) Perceptual objective listening quality assessment (polqa), the third generation itu-t standard for end-to-end speech quality measurement part i–temporal alignment. J Audio Eng Soc 61(6):366–384
11.
Zhang X, Chen X, Wang Y, Gui G, Adebisi B, Sari H, Adachi F (2023) Lightweight automatic modulation classification via progressive differentiable architecture search. IEEE Transactions on Cognitive Communications and Networking
12.
Wang J, Zha H, Fu J (2022) Evaluation of deep learning model in the field of electromagnetic signal recognition. In: IEEE INFOCOM 2022-IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 1–6. IEEE
13.
Lin Y, Tu Y, Dou Z (2020) An improved neural network pruning technology for automatic modulation classification in edge devices. IEEE Trans Veh Technol 69(5):5703–5706CrossRefMATH
14.
Lin Y, Tu Y, Dou Z, Chen L, Mao S (2020) Contour stella image and deep learning for signal recognition in the physical layer. IEEE Trans Cogn Commun Netw 7(1):34–46CrossRefMATH
15.
Yao Z, Fu X, Guo L, Wang Y, Lin Y, Shi S, Gui G (2023) Few-shot specific emitter identification using asymmetric masked auto-encoder. IEEE Communications Letters
16.
Lin Y, Zhao H, Ma X, Tu Y, Wang M (2020) Adversarial attacks in modulation recognition with convolutional neural networks. IEEE Trans Reliab 70(1):389–401CrossRefMATH
17.
Liu C, Fu X, Wang Y, Guo L, Liu Y, Lin Y, Zhao H, Gui G (2023) Overcoming data limitations: a few-shot specific emitter identification method using self-supervised learning and adversarial augmentation. IEEE Transactions on Information Forensics and Security
18.
Ya T, Yun L, Haoran Z, Zhang J, Yu W, Guan G, Shiwen M (2022) Large-scale real-world radio signal recognition with deep learning. Chin J Aeronaut 35(9):35–48CrossRefMATH
19.
Chen Y, Sun J, Lin Y, Gui G, Sari H (2021) Hybrid n-inception-lstm-based aircraft coordinate prediction method for secure air traffic. IEEE Trans IntellTrans Syst 23(3):2773–2783CrossRefMATH
20.
Avila AR, Gamper H, Reddy C, Cutler R, Tashev I, Gehrke J (2019) Non-intrusive speech quality assessment using neural networks. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 631–635. IEEE
21.
Fu S-W, Tsao Y, Hwang H-T, Wang H-M (2018) Quality-net: An end-to-end non-intrusive speech quality assessment model based on blstm. arXiv:​1808.​05344
22.
Jia X, Li D (2020) A deep learning-based time-domain approach for non-intrusive speech quality assessment. In: 2020 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC), pp 477–481. IEEE
23.
Serrá J, Pons J, Pascual S (2021) Sesqa: semi-supervised learning for speech quality assessment. In: ICASSP 2021-2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 381–385. IEEE
24.
Chiang H-T, Wu Y-C, Yu C, Toda T, Wang H-M, Hu Y-C, Tsao Y (2021) Hasa-net: A non-intrusive hearing-aid speech assessment network. In: 2021 IEEE automatic speech recognition and understanding workshop (ASRU), pp 907–913. IEEE
25.
Choi Y, Jung Y, Suh Y, Kim H (2022) Learning to maximize speech quality directly using mos prediction for neural text-to-speech. IEEE Access 10:52621–52629CrossRefMATH
26.
Reddy CK, Gopal V, Cutler R (2022) Dnsmos p. 835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 886–890. IEEE
27.
Jaiswal RK, Dubey RK (2023) Caqoe: A novel no-reference context-aware speech quality prediction metric. ACM Trans Multimed Comput Commun Appl 19(1s):1–23CrossRefMATH
28.
Jaiswal RK, Dubey RK (2023) Multiple time-instances features based approach for reference-free speech quality measurement. Comput Speech & Lang 79:101478CrossRefMATH
29.
Hao J, Ye S, Lu C, Dong F, Liu J, Pi D (2022) Soft-label learn for no-intrusive speech quality assessment. Proc Interspeech 2022:3303–3307MATH
30.
Albuquerque RQ, Mello CA (2021) Automatic no-reference speech quality assessment with convolutional neural networks. Neural Comput Appl 33:9993–10003CrossRefMATH
31.
Liao S, Chen F (2022) Assessing the effect of temporal misalignment between the probe and processed speech signals on objective speech quality evaluation. In: 2022 13th International symposium on chinese spoken language processing (ISCSLP), pp 140–144. IEEE
32.
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2010) An image is worth 16x16 words: Transformers for image recognition at scale. arxiv 2020. arXiv:​2010.​11929
Metadaten
Titel
Intelligent Assessment Method of Communication Interference Speech Quality Based on End-to-end Network
verfasst von
Sen Wang
Jianying Tao
Zheng Dou
Jiangzhi Fu
Publikationsdatum
11.01.2025
Verlag
Springer US
Erschienen in
Mobile Networks and Applications
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-024-02338-4