Empirical mode decomposition based statistical features for discrimination of speech and low frequency music signal (original) (raw)

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References

  1. Alexandre-Cortizo E, Rosa-Zurera M, Lopez-Ferreras F (2005) Application of fisher linear discriminant analysis to speech/music classification. EUROCON 2005 - The International Conference on "Computer as a Tool", pp 1666–1669. https://doi.org/10.1109/EURCON.2005.1630291
  2. Babiker A, Faye I, Mumtaz W, Malik AS, Sato H (2018) EEG in classroom: EMD features to detect situational interest of students during learning. Multimedia Tools and Applications, pp:1–21
  3. Birajdar GK, Patil MD, (2018) Speech and music classification using spectrogram based statistical descriptors and extreme learning machine. Multimedia tools and applications, pp.1-28.
  4. Bouzid A, Ellouze N (2004) “Empirical mode decomposition of voiced speech signal,” in Control, Communications and Signal Processing, 2004. First International Symposium on. IEEE, pp. 603–606
  5. Bykhovsky D, Hadar O (2010) Evaluation of a GLRT threshold for voiced-unvoiced decision and pitch tracking in noisy speech. 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, pp 000680–000683. https://doi.org/10.1109/EEEI.2010.5662126
  6. Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. Signal Processing Letters, IEEE 11(2):112–114
    Article Google Scholar
  7. Gu Q, Li Z, Han J, (2012) Generalized fisher score for feature selection. arXiv preprint arXiv:1202.3725. https://doi.org/10.48550/arXiv.1202.3725
  8. Huang NE, (2014) Hilbert-Huang transform and its applications (Vol. 16). World scientific
  9. Huang H, Pan J (2006) Speech pitch determination based on Hilbert Huang transform. Signal Process 86(4):792–803
    Article MATH Google Scholar
  10. Huang NE, Shen SS (2005) Hilbert-Huang transform and its applications. World Scientific 5
  11. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In proceedings of the Royal Society of London a: mathematical, physical and engineering sciences. R Soc 454(1971):903–995
  12. Khonglah BK, Prasanna SM (2016) Speech/music classification using speech-specific features. Digital Signal Processing 48:71–83
    Article MathSciNet Google Scholar
  13. Khonglah BK, Sharma R, Mahadeva Prasanna SR, (2015) Speech vs music discrimination using empirical mode decomposition. 2015 Twenty First National Conference on Communications (NCC), pp 1–6. https://doi.org/10.1109/NCC.2015.7084865
  14. Kim SK, Chang JH (2009) Speech/music classification enhancement for 3GPP2 SMV codec based on support vector machine. IEICE Trans Fundam Electron Commun Comput Sci 92(2):630–632. https://doi.org/10.1587/transfun.E92.A.630
  15. Lahmiri S, Gargour C, Gabrea M, (2012) Statistical features selection from intrinsic mode functions for pathologies detection in retina digital images. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, pp 1585–1590. https://doi.org/10.1109/IECON.2012.6388532
  16. Lim C, Chang JH (2015) Efficient implementation techniques of an svm-based speech/music classifier in SMV. Multimed Tools Appl 74(15):5375–5400
    Article Google Scholar
  17. Moreno PJ, Rifkin R, (2000) Using the fisher kernel method for web audio classification. In acoustics, speech, and signal processing, 2000. ICASSP'00. Proceedings. 2000 IEEE international conference on (Vol. 4, pp. 2417-2420). IEEE
  18. Panagiotakis C, Tziritas G (2002) A speech/music discriminator based on RMS and zero-crossings. 2002 11th European Signal Processing Conference, pp 1-4
  19. Pantazis Y, Rosec O, Stylianou Y (2011) Adaptive AM–FM signal decomposition with application to speech analysis. IEEE Trans Audio Speech Lang Process 19(2):290–300
    Article Google Scholar
  20. Papakostas M, Giannakopoulos T (2018) Speech-music discrimination using deep visual feature extractors. Expert Syst Appl 114:334–344
    Article Google Scholar
  21. Roffo G, Melzi S, (2017) Ranking to learn: feature ranking and selection via eigenvector centrality. In new Frontiers in mining complex patterns: 5th international workshop, NFMCP 2016, held in conjunction with ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016, revised selected papers (Vol. 10312, p. 19). Springer
  22. Ruiz-Reyes N, Vera-Candeas P, Muñoz JE, García-Galán S, Cañadas FJ (2009) New speech/music discrimination approach based on fundamental frequency estimation. Multimed Tools Appl 41(2):253–286
    Article Google Scholar
  23. Sahoo JP, Ari S, Ghosh DK (2018) Hand gesture recognition using DWT and F-ratio based feature descriptor. IET Image Process 12(10):1780–1787
    Article Google Scholar
  24. Saunders J, (1996) Real-time discrimination of broadcast speech/music. In ICASSP (pp. 993-996). IEEE
  25. Scheirer E, Slaney M (1997) Construction and evaluation of a robust multi-feature speech/music discriminator. In Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on. IEEE 2:1331–1334
  26. Seck M, Bimbot F, Zugaj D, Delyon B, (1999) Two-class signal segmentation for speech/music detection in audio tracks. In Sixth European Conference on Speech Communication and Technology
  27. Sharma R, Prasanna SM, (2015) Characterizing glottal activity from speech using empirical mode decomposition. In communications (NCC), 2015 twenty first National Conference on (pp. 1-6). IEEE
  28. Shirazi J, Ghaemmaghami S (2010) Improvement to speech-music discrimination using sinusoidal model based features. Multimed Tools Appl 50(2):415–435. https://doi.org/10.1007/s11042-009-0416-3
    Article Google Scholar
  29. Tsipas N, Vrysis L, Dimoulas C, Papanikolaou G (2017) Efficient audio-driven multimedia indexing through similarity-based speech/music discrimination. Multimed Tools Appl 76(24):25603–25621. https://doi.org/10.1007/s11042-016-4315-0
    Article Google Scholar
  30. Wang G, Chen XY, Qiao FL, Wu Z, Huang NE (2010) On intrinsic mode function. Adv Adapt Data Anal 2(03):277–293
    Article MathSciNet Google Scholar
  31. Williams G, Ellis DP, (1999) Speech/music discrimination based on posterior probability features. Eurospeech 99: 6th European Conference on Speech Communication and Technology: Budapest, Hungary, September 5–9. https://doi.org/10.7916/D8KH0XRH
  32. Wu Z, Huang NE (2004) A study of the characteristics of white noise using the empirical mode decomposition method. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences 460(2046):1597–1611
    Article MATH Google Scholar
  33. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41. https://doi.org/10.1142/S1793536909000047
    Article Google Scholar
  34. YouTube. (2019). Relaxing Music from Sungha Jung (The Best of). [Online] Available at: https://www.youtube.com/watch?v=IP8vBL5Q8Ac&t=338s. Accessed 05 Jan 2021
  35. Zhang T, Kuo CCJ (2001) Audio content analysis for online audiovisual data segmentation and classification. IEEE Transactions on speech and audio processing 9(4):441–457
    Article Google Scholar

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