Combining Evidence from Temporal and Spectral Features for Person Recognition Using Humming

  • Hemant A. Patil
  • Maulik C. Madhavi
  • Rahul Jain
  • Alok K. Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7143)


In this paper, hum of a person is used to identify a speaker with the help of machine. In addition, novel temporal features (such as zero-crossing rate & short-time energy) and spectral features (such as spectral centroid & spectral flux) are proposed for person recognition task. Feature-level fusion of each of these features with state-of-the art spectral feature set, viz., Mel Frequency Cepstral Coefficients (MFCC) is found to give better recognition performance than MFCC alone. In addition, it is shown that the person identification rate is competitive over baseline MFCC. Furthermore, the reduction in equal error rate (EER) by 1.46 % is obtained when a feature-level fusion system is employed by combining evidences from MFCC, temporal and proposed spectral features.


Humming Mel cepstrum zero-crossing rate short-time energy spectral centroid spectral flux and polynomial classifier 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amino, K., Arai, T.: Perceptual Speaker Identification Using Monosyllabic Stimuli-Effects of the Nucleus Vowels and Speaker Characteristics Contained in Nasals. In: INTERSPEECH 2008, Brisbane, Australia, pp. 1917–1920 (2008)Google Scholar
  2. 2.
    Patil, H.A., Jain, R., Jain, P.: Identification of Speakers from their Hum. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2008. LNCS (LNAI), vol. 5246, pp. 461–468. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Jin, M., Kim, J., Yoo, C.D.: Humming-based Human Verification and Identification. In: Proc. Int. Conf. on Acoustic, Speech and Signal Processing, ICASSP 2009, Taipei, Taiwan, pp. 1453–1456 (2009)Google Scholar
  4. 4.
    Patil, H.A., Parhi, K.K.: Novel Variable Length Teager Energy based Features for Person Recognition from Their Hum. In: Proc. Int. Conf. on Acoustic, Speech and Signal Processing, ICASSP 2010, Dallas, Texas, USA, pp. 4526–4529 (2010)Google Scholar
  5. 5.
    Huang, R., Hansen, J.H.L.: Advances in Unsupervised Audio Classification and Segmentation for the Broadcast News and NGSW Corpora. IEEE Transactions on Audio, Speech, and Language Processing 14(3), 907–919 (2006)CrossRefGoogle Scholar
  6. 6.
    Kedem, B.: Spectral Analysis and Discrimination by Zero-Crossings. Proc. IEEE 74(11), 1477–1493 (1986)CrossRefGoogle Scholar
  7. 7.
    Schubert, E., Wolfe, J., Tarnopolsky, A.: Spectral Centroid and Timbre in Complex, Multiple Instrumental Textures. In: Proceedings of the 8th International Conference on Music Perception & Cognition, Evanston, IL, pp. 654–657 (2004)Google Scholar
  8. 8.
    Davis, S.B., Mermelstein, P.: Comparison on Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences. IEEE, Transactions on Acoustics, Speech, And Signal Processing ASSP-28(4), 357–366 (1980)CrossRefGoogle Scholar
  9. 9.
    Campbell, W.M., Assaleh, K.T., Broun, C.C.: Speaker Recognition with Polynomial Classifiers. IEEE Transactions on Speech and Audio Processing 10(4), 205–212 (2002)CrossRefGoogle Scholar
  10. 10.
    Martin, A.F., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The DET Curve in Assessment of Detection Task Performance. In: Proc. EUROSPEECH 1997, Rhodes, Greece, vol. 4, pp. 1895–1898 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hemant A. Patil
    • 1
  • Maulik C. Madhavi
    • 1
  • Rahul Jain
    • 2
  • Alok K. Jain
    • 3
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyIndia
  2. 2.Hindustan Institute of Technology and ManagementAgraIndia
  3. 3.Nikhil Institute of Engineering and ManagementMathuraIndia

Personalised recommendations