Advertisement

Feature Extraction

  • Soumya Sen
  • Anjan Dutta
  • Nilanjan Dey
Chapter
First Online:
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

In order to classify any audio or speech signal, feature extraction is the prerequisite. The analog speech signal s(t) is sampled a number of times per second to be stored in some recording device or simply on a computer.

This is a preview of subscription content, log in to check access.

References

  1. 1.
    De Poli, G., & Mion, L. (2006). From audio to content. Livro não publicado. Padova: Dipartimento di Ingegneria Dell’Informazione-Università degli Studi di Padova.Google Scholar
  2. 2.
    Song, Y., Wang, W. H., & Guo, F. J. (2009). Feature extraction and classification for audio information in news video. In 2009 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2009 (pp. 43–46). IEEE.Google Scholar
  3. 3.
    Burred, J. J., & Lerch, A. (2004). Hierarchical automatic audio signal classification. Journal of the Audio Engineering Society, 52(7/8), 724–739.Google Scholar
  4. 4.
    Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293–302.CrossRefGoogle Scholar
  5. 5.
    Galembo, A., & Askenfelt, A. (1994). Measuring inharmonicity through pitch extraction. Journal STL-QPSR, 35(1), 135–144.Google Scholar
  6. 6.
    Fletcher, H. (1964). Normal vibration frequencies of a stiff piano string. The Journal of the Acoustical Society of America, 36(1), 203–209.CrossRefGoogle Scholar
  7. 7.
    Retrieved September 09, 2018, from https://pages.mtu.edu/~suits/autocorrelation.html.
  8. 8.
    American National Standards Institute. (1973). American national psychoacoustical terminology S3. 20.Google Scholar
  9. 9.
    Lazaro, A., Sarno, R., Andre, R. J., & Mahardika, M. N. (2017). Music tempo classification using audio spectrum centroid, audio spectrum flatness, and audio spectrum spread based on MPEG-7 audio features. In 2017 3rd International Conference on Science in Information Technology (ICSITech) (pp. 41–46). IEEE.Google Scholar
  10. 10.
    Burred, J. J., & Lerch, A. (2004). Hierarchical automatic audio signal classification. Journal of the Audio Engineering Society, 52(7/8), 724–739.Google Scholar
  11. 11.
    Chauhan, P. M., & Desai, N. P. (2014). Mel frequency cepstral coefficients (mfcc) based speaker identification in noisy environment using wiener filter. In 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) (pp. 1–5). IEEE.Google Scholar
  12. 12.
    Lindblom, B., Sundberg, J., Branderud, P., Djamshidpey, H., & Granqvist, S. (2010). The Gunnar Fant legacy in the study of vocal acoustics. In 10ème Congrès Français d’Acoustique.Google Scholar
  13. 13.
  14. 14.
  15. 15.
    Bradbury, J. (2000). Linear predictive coding. Hill: Mc G.Google Scholar
  16. 16.
    Kumar, C. S., & Rao, P. M. (2011). Design of an automatic speaker recognition system using MFCC, vector quantization and LBG algorithm. International Journal on Computer Science and Engineering, 3(8), 2942.Google Scholar
  17. 17.
    Shrawankar, U., & Thakare, V. M. (2013). Techniques for feature extraction in speech recognition system: A comparative study. arXiv preprint arXiv:1305.1145.
  18. 18.
    Benba, A., Jilbab, A., & Hammouch, A. (2014). Voice analysis for detecting persons with Parkinson’s disease using MFCC and VQ. In The 2014 International Conference on Circuits, Systems and Signal Processing (pp. 23–25).Google Scholar
  19. 19.
    Young, S., et al. (2006). The HTK book (v3. 4). Cambridge University.Google Scholar
  20. 20.
    Brigham, E. O., & Morrow, R. E. (1967). The fast Fourier transform. IEEE spectrum, 4(12), 63–70.CrossRefGoogle Scholar
  21. 21.
    Retrieved September 16, 2018, from kom.aau.dk/group/04gr742/pdf/MFCC_worksheet.pdf.
  22. 22.
    Wanli, Z., & Guoxin, L. (2013). The research of feature extraction based on MFCC for speaker recognition. In 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT) (pp. 1074–1077). IEEE.Google Scholar
  23. 23.
    Paliwal, K. K. (1982). On the performance of the quefrency-weighted cepstral coefficients in vowel recognition. Speech Communication, 1(2), 151–154.MathSciNetCrossRefGoogle Scholar
  24. 24.
    Tohkura, Y. (1987). A weighted cepstral distance measure for speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(10), 1414–1422.CrossRefGoogle Scholar
  25. 25.
    Juang, B. H., Rabiner, L., & Wilpon, J. G. (1986). On the use of bandpass liftering in speech recognition. In IEEE International Conference on ICASSP’86 Acoustics, Speech, and Signal Processing (Vol. 11, pp. 765–768). IEEE.Google Scholar
  26. 26.
    Itakura, F., & Umezaki, T. (1987). Distance measure for speech recognition based on the smoothed group delay spectrum. In IEEE International Conference on ICASSP’87 Acoustics, Speech, and Signal Processing (Vol. 12, pp. 1257–1260). IEEE.Google Scholar
  27. 27.
    Hanson, B., & Wakita, H. (1987). Spectral slope distance measures with linear prediction analysis for word recognition in noise. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(7), 968–973.CrossRefGoogle Scholar
  28. 28.
    Paliwal, K. K. (1999). Decorrelated and liftered filter-bank energies for robust speech recognition. In Sixth European Conference on Speech Communication and Technology.Google Scholar
  29. 29.
    Hermansky, H. (1990). Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America, 87(4), 1738–1752.CrossRefGoogle Scholar
  30. 30.
    Zwicker, E. (1961). Subdivision of the audible frequency range into critical bands (Frequenzgruppen). The Journal of the Acoustical Society of America, 33(2), 248–248.CrossRefGoogle Scholar
  31. 31.
    Hermes, D. J. Sound Perception: The Science of Sound Design.Google Scholar
  32. 32.
    Stevens, S. S. (1957). On the psychophysical law. Psychological Review, 64(3), 153.CrossRefGoogle Scholar
  33. 33.
    Graps, A. (1995). An introduction to wavelets. IEEE Computational Science and Engineering, 2(2), 50–61.CrossRefGoogle Scholar
  34. 34.
    Polikar, R. (1996). Fundamental concepts & an overview of the wavelet theory. In The Wavelet Tutorial Part I. Rowan University, College of Engineering Web Servers (vol. 15).Google Scholar
  35. 35.
    Avci, E., & Akpolat, Z. H. (2006). Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Systems with Applications, 31(3), 495–503.CrossRefGoogle Scholar
  36. 36.
    Siafarikas, M., Ganchev, T., & Fakotakis, N. (2004). Wavelet packet based speaker verification. In ODYSSEY04-The Speaker and Language Recognition Workshop.Google Scholar
  37. 37.
    Buckheit, J. B., & Donoho, D. L. (1995). Wavelab and reproducible research. In Wavelets and statistics (pp. 55–81). New York: Springer.CrossRefGoogle Scholar
  38. 38.
    Wesfreid, E., & Wickerhauser, M. V. (1993). Adapted local trigonometric transforms and speech processing. IEEE Transactions on Signal Processing, 41(12), 3596–3600.CrossRefGoogle Scholar
  39. 39.
    Visser, E., Otsuka, M., & Lee, T. W. (2003). A spatio-temporal speech enhancement scheme for robust speech recognition in noisy environments. Speech Communication, 41(2–3), 393–407.CrossRefGoogle Scholar
  40. 40.
    Tufekci, Z., & Gowdy, J. N. (2000). Feature extraction using discrete wavelet transform for speech recognition. In Proceedings of the IEEE Southeastcon 2000 (pp. 116–123). IEEE.Google Scholar
  41. 41.
    El-Attar, A., Ashour, A. S., Dey, N., Abdelkader, H., Abd El-Naby, M. M., & Sherratt, R. S. (2018). Discrete wavelet transform-based freezing of gait detection in Parkinson’s disease. Journal of Experimental & Theoretical Artificial Intelligence, 1–17.Google Scholar
  42. 42.
    Mukhopadhyay, S., Biswas, S., Roy, A. B., & Dey, N. (2012). Wavelet based QRS complex detection of ECG signal. arXiv preprint arXiv:1209.1563.
  43. 43.
    Rady, E. R., Yahia, A. H., El-Sayed, A., & El-Borey, H. Speech recognition system based on wavelet transform and artificial neural network.Google Scholar
  44. 44.
    Zbancioc, M., & Costin, M. (2003). Using neural networks and LPCC to improve speech recognition. In 2003 International Symposium on Signals, Circuits and Systems, SCS 2003 (Vol. 2, pp. 445–448). IEEE.Google Scholar
  45. 45.
    Paul, A. K., Das, D., & Kamal, M. M. (2009). Bangla speech recognition system using LPC and ANN. In Seventh International Conference on Advances in Pattern Recognition, 2009. ICAPR’09 (pp. 171–174). IEEE.Google Scholar
  46. 46.
    Kuo, K. (2010). Feature extraction and recognition of infant cries. In 2010 IEEE International Conference on Electro/Information Technology (EIT) (pp. 1–5). IEEE.Google Scholar
  47. 47.
    Hossan, M. A., Memon, S., & Gregory, M. A. (2010). A novel approach for MFCC feature extraction. In 2010 4th International Conference on Signal Processing and Communication Systems (ICSPCS) (pp. 1–5). IEEE.Google Scholar
  48. 48.
    Wanli, Z., & Guoxin, L. (2013). The research of feature extraction based on MFCC for speaker recognition. In 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT) (pp. 1074–1077). IEEE.Google Scholar
  49. 49.
    Sharma, D., & Ali, I. (2015). A modified MFCC feature extraction technique for robust speaker recognition. In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1052–1057). IEEE.Google Scholar
  50. 50.
    Najafi, J., & Marvi, H. (2009). PLP based CELP speech coder. In 2009 Second International Conference on Computer and Electrical Engineering, ICCEE’09 (Vol. 1, pp. 263–267). IEEE.Google Scholar
  51. 51.
    Chaloupka, J., Červa, P., Silovský, J., Žd’ánský, J., & Nouza, J. (2012). Modification of the speech feature extraction module for the improvement of the system for automatic lectures transcription. In ELMAR, 2012 Proceedings (pp. 223–226). IEEE.Google Scholar
  52. 52.
    Saeidi, R., Alku, P., & Bäckström, T. (2016). Feature extraction using power-law adjusted linear prediction with application to speaker recognition under severe vocal effort mismatch. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24(1), 42–53.CrossRefGoogle Scholar
  53. 53.
    Gang, R., Bocko, M. F., & Headlam, D. (2010). Reverberation features identification from music recordings using the discrete wavelet transform. In 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) (pp. 161–164). IEEE.Google Scholar
  54. 54.
    Nehe, N. S., & Holambe, R. S. (2012). DWT and LPC based feature extraction methods for isolated word recognition. EURASIP Journal on Audio, Speech, and Music Processing, 2012(1), 7.CrossRefGoogle Scholar
  55. 55.
    Kristomo, D., Hidayat, R., & Soesanti, I. (2016). Feature extraction and classification of the Indonesian syllables using Discrete Wavelet Transform and statistical features. In International Conference on Science and Technology-Computer (ICST) (pp. 88–92). IEEE.Google Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Soumya Sen
    • 1
  • Anjan Dutta
    • 2
  • Nilanjan Dey
    • 3
  1. 1.A.K. Choudhury School of Information TechnologyUniversity of CalcuttaKolkataIndia
  2. 2.Department of Information TechnologyTechno India College of TechnologyKolkataIndia
  3. 3.Department of Information TechnologyTechno India College of TechnologyKolkataIndia

Personalised recommendations

Cite chapter

Buy options