Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22115–22129 | Cite as

Effective pattern recognition and find-density-peaks clustering based blind identification for underdetermined speech mixing systems

  • Xiangdong HuangEmail author
  • Lin Yang
  • Runan Song
  • Wei Lu


In order to achieve high-efficiency blind identification (BI) for underdetermined speech mixing systems without recovery degradation, this paper proposes a novel BI scheme based on effective pattern recognition and the find-density-peaks (FDP) clustering algorithm. To lower BI’s computational complexity, a 3-step effective pattern recognition procedure is proposed, which consists of voiced-sound pattern sifting, spectrum correction based harmonic representation and phase uniformity based single-active-source (SAS) pattern recognition. Furthermore, a 5-step FDP clustering procedure is summarized and utilized to determine the souce number and estimate all the columns of the mixing matrix. Our experimental results showed that, the proposed 3-step effective pattern recognition procedure can condense the original 56383 TF patterns into only 194 effective SAS patterns, which considerably alleviates the computational burden of BI. Moreover, by means of FDP clustering, not only the source number can be intuitively and readily determined, but also the mixing matrix can be estimated with a higher recovery SNR than the existing BI schemes. Due to harmonic-like components are of wide applications, our proposed BI scheme possesses a vast potential in other harmonics-related blind-signal-separation (BSS) fields such as mechanical vibration analysis, channel estimation in communication.


Underdetermined blind identification Phase uniformity Harmonics Find-density-peaks (FDP) clustering 



This work was financially supported by Qingdao National Laboratory for Marine Science and Technology under Grant No. QNLM2016OPR0411.


  1. 1.
    Abrard F, Deville Y (2005) A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources. Signal Process 85 (7):1389–1403CrossRefzbMATHGoogle Scholar
  2. 2.
    Aïssa-El-Bey A, Linh-Trung N, Abed-Meraim K, Belouchrani A, Grenier Y (2007) Underdetermined blind separation of nondisjoint sources in the time-frequency domain. IEEE Trans Signal Process 55(3):897–907MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bofill P, Zibulevsky M (2001) Underdetermined blind source separation using sparse representations. Signal Process 81(11):2353–2362CrossRefzbMATHGoogle Scholar
  4. 4.
    Florea C, Gordan M, Vlaicu A, Orghidan R (2014) Computationally efficient formulation of sparse color image recovery in the JPEG compressed domain. J Math Imaging Vision 49(1):173–190MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Gao Z, Zhang H, Xu G, Xue Y, Hauptmannc AG (2015) Multi-view discriminative and structured dictionary learning with group sparsity for human action recognition. Signal Process 112:83–97CrossRefGoogle Scholar
  6. 6.
    Gao Z, Zhang L, Chen M, Hauptmann A, Zhang H, Cai A (2014) Enhanced and hierarchical structure algorithm for data imbalance problem in semantic extraction under massive video dataset. Multimedia Tools and Applications 68(3):641–657CrossRefGoogle Scholar
  7. 7.
    Ge S, Han J, Han M (2015) Nonnegative mixture for underdetermined blind source separation based on a tensor algorithm. Circuits Systems & Signal Processing 34 (9):2935–2950MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Hayes M, Lim Jae, Oppenheim A (1980) Signal reconstruction from phase or magnitude. IEEE Trans Acoust Speech Signal Process 28(6):672–680MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    He Z, Cichocki A, Zdunek R, Xie S (2009) Improved FOCUSS method with conjugate gradient iterations. IEEE Trans Signal Process 57(1):399–404MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Jourjine A, Rickard S, Yılmaz Ö (2000) Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures. In: ICASSP, pp 2985–2988Google Scholar
  11. 11.
    Koeipensri T, Boonchoo P, Sueaseenak D (2016) The development of biosignal processing system (BPS-SWU v1.0) for learning and research in biomedical engineering. In: 2016 9th biomedical engineering international conference, pp 1–4Google Scholar
  12. 12.
    Liu AA, Nie WZ, Gao Y, Su YT (2016) Multi-modal clique-graph matching for view-based 3D model retrieval. IEEE Trans Image Process 25(5):2103–2116MathSciNetCrossRefGoogle Scholar
  13. 13.
    Liu AA, Su YT, Jia PP, Gao Z, Hao T, Yang ZX (2015) Multipe/single-view human action recognition via part-induced Multitask structural learning. IEEE Transactions on Cybernetics 45(6):1194–1208CrossRefGoogle Scholar
  14. 14.
    Liu AA, Su YT, Nie WZ, Kankanhalli M (2017) Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans Pattern Anal Mach Intell 39(1):102–114CrossRefGoogle Scholar
  15. 15.
    Liu B, Reju VG, Khong AWH (2014) A linear source recovery method for underdetermined mixtures of uncorrelated AR-model signals without sparseness. IEEE Trans Signal Process 62(19):4947–4958MathSciNetCrossRefGoogle Scholar
  16. 16.
    Mohimani H, Babaie-Zadeh M, Jutten C (2009) A fast approach for overcomplete sparse decomposition based on smoothed 0 norm. IEEE Trans Signal Process 57(1):289–301MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    O’Grady PD, Pearlmutter BA (2008) The LOST algorithm: finding lines and separating speech mixtures. EURASIP Journal on Advances in Signal Processing 2008 (1):1–17zbMATHGoogle Scholar
  18. 18.
    Qiao ZJ, Lei YG, Lin J, Jia F (2016) An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis. Mech Syst Signal Process 84(Part A):731–746Google Scholar
  19. 19.
    Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496CrossRefGoogle Scholar
  20. 20.
    Saab R, Yılmaz Ö, McKeown MJ, Abugharbieh R (2007) Underdetermined anechoic blind source separation via q-basis-pursuit with q < 1. IEEE Trans Signal Process 55(8):4004–4017MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Sha Z, Huang Z, Zhou Y, Wang F (2013) Frequency-hopping signals sorting based on underdetermined blind source separation. IET Commun 7(14):1456–1464CrossRefGoogle Scholar
  22. 22.
    Siegel LJ, Bessey A (1982) Voiced/unvoiced/mixed excitation classification of speech. IEEE Trans Acoust Speech Signal Process 30(3):451–460CrossRefGoogle Scholar
  23. 23.
    Vaseghi SV (2008) Advanced digital signal processing and noise reduction. Wiley, New YorkCrossRefGoogle Scholar
  24. 24.
    Xie S, Yang L, Yang J, Zhou G, Xiang Y (2012) Time-frequency approach to underdetermined blind source separation. IEEE Transactions on Neural Networks & Learning Systems 23(2):306–316CrossRefGoogle Scholar
  25. 25.
    Xu ZJ, Gong Y, Wang K, Lu WD, Hua JY (2017) Covert digital communication systems based on joint normal distribution. IET Commun 11 (8):1282–1290CrossRefGoogle Scholar
  26. 26.
    Yang Y, Song J, Huang Z, Ma Z, Sebe N, Hauptmann AG (2013) Multi-feature fusion via hierarchical regression for multimedia analysis. IEEE Trans Multimedia 15(3):572–581CrossRefGoogle Scholar
  27. 27.
    Yılmaz Ö, Rickard S (2004) Blind sepraration of speech mixtures via time-frequency masking. IEEE Trans Signal Process 52(7):1830–1847MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Zhang F, Geng Z, Yuan W (2001) The algorithm of interpolating windowed FFT for harmonic analysis of electric power system. IEEE Trans Power Delivery 16 (2):160–164CrossRefGoogle Scholar
  29. 29.
    Zhou G, Yang Z, Xie S, Yang J (2011) Mixing matrix estimation from sparse mixtures with unknown number of sources. IEEE Trans Neural Netw 22(2):211–221CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringTianjin UniversityTianjinChina

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