Skip to main content
SpringerLink
Log in

Single-channel speech separation using empirical mode decomposition and multi pitch information with estimation of number of speakers

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

Speech separation is an essential part of any voice recognition system like speaker recognition, speech recognition and hearing aids etc. When speech separation is applied at the front-end of any voice recognition system increases the performance efficiency of that particular system. In this paper we propose a system for single channel speech separation by combining empirical mode decomposition (EMD) and multi pitch information. The proposed method is completely unsupervised and requires no knowledge of the underlying speakers. In this method we apply EMD to short frames of the mixed speech for better estimation of the speech specific information. Speech specific information is derived through multi pitch tracking. To track multi pitch information from the mixed signal we apply simple-inverse filtering tracking and histogram based pitch estimation to excitation source information along with estimating the number of speakers present in the mixed signal.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31

Similar content being viewed by others

References

  • Bofill, P. (2008). Identifying single source data for mixing matrix estimation in instantaneous blind source separation. Proceedings of the ICANN, 5163, 759–767.

    Google Scholar 

  • Boldt, J. B., & Ellis, D. P. W. (2009) A simple correlation based model of intelligibility for nonlinear speech enhancement and separation. Columbia university academic commons, http://hdl.handle.net/10022/AC:P:13660.

  • Digital Speech Processing Course (2015). Time domain methods in speech processing. http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/speech20course.html.

  • Douglas, S. C., Sawada, H., & Makino, S. (2005). Natural gradient Multichannel blind deconvolution and speech separation using causal FIR filters. IEEE Transactions on Speech Audio Processing, 13(1), 92–104.

    Article  Google Scholar 

  • Ellis, D. (2006). Model based scene analysis. In D. Wang & G. Brown (Eds.), Computational auditory scene analysis: Principles, algorithms and applications. New York: Wiley.

    Google Scholar 

  • Fevotte, C., & Godsill, S. J. (2006). A baysean approach for blind separation of sparse sources. IEEE Transactions on Audio, Speech and Language Processing, 14(6), 2174–2188.

    Article  Google Scholar 

  • Gao, B., Woo, W. L., & Dlay, S. S. (2011). Single channel source separation using EMD sub band variable regularized sparse features. IEEE Transactions on Audio, Speech and Language Processing, 19(4), 961–976.

    Article  Google Scholar 

  • Gao, B., Woo, W. L., & Dlay, S. S. (2013). Unsupervised single Channel separation of non stationary signals using Gammatone filter bank and Itakura-Satio nonnegative matrix two-dimensional factorizations. IEEE Transactions on Circuits and Systems, 60(3), 662–675.

    Article  MathSciNet  Google Scholar 

  • Greenwood M., & Kinghorn, A. (1999). SUVing: Automatic Silence/Unvoiced/Voiced Classification of Speech. Undergraduate Coursework, Department of Computer Science, The University of Sheffield, http://www.dcs.shef.ac.uk/mark/uni/.

  • Hershey, J.R., Olsen, P.A., Rennie, S. J., & Aron, A. (2011). Audio Alchemy: Getting computers to understand overlapping speech. Scientific American Online, http://www.scientificamerican.com/article/speech-gettingcomputersunderstand-overlapping.

  • Huang, N. E., Shen, Z., & Long, S. R. (1998). The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of Royal Society of London, 454, 903–995.

    Article  MathSciNet  MATH  Google Scholar 

  • Jang, G. J., & Lee, T. W. (2003). A maximum likelihood approach to single channel source separation. Journal of Machine Learning Research, 4, 1365–1392.

    MathSciNet  MATH  Google Scholar 

  • Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: John Wiley Sons.

    MATH  Google Scholar 

  • Kristjansson, T., Attias, H., & Hershey, J. (2004) Single microphone source separation using high resolution signal reconstruction. In Proceedings of International Conference on Acoustics, Speech, Signal Processing, (ICASSP’04, (Vol. 2, pp. 817–820). Montreal, QC.

  • Kumaraswamy, R., Yegnanarayana, B., & Sri ramamurty, K. (2009). Determining mixing parameters from multi speaker data using speech specific information. IEEE Transactions on Audio Speech and Language Processing, 17(6), 1196–1207.

    Article  Google Scholar 

  • Li, Y., Amari, S., & Cichocki, A. (2006a). Underdetermined blind source separation based on sparse representation. IEEE Transactions on Audio, Speech and Language Processing, 54(2), 423–437.

    Google Scholar 

  • Li, P., Guan, Y., & Xu, B. (2006b). Monaural speech separation based on computational auditory scene analysis and objective quality assessment of speech. IEEE Transactions on Audio, Speech and Language Processing, 14(6), 2014–2023.

    Article  Google Scholar 

  • Linear Prediction Analysis (2015) http://iitg.vlab.co.in/?sub=59&brch=164&sim=616&cnt=1108.

  • Litvin, Y., & Cohen, I. (2009). Single channel source separation of audio signals using Bark Scale Wavlet Packet Decomposition. IEEE International Workshop on Machine Learning for Signal Processing, 65(3), 339–9350.

    Google Scholar 

  • Mijovic, Bogdan, & De Vos, Maarten. (2010). Source separation from single channel recordings by combining empirical mode decomposition and independent component analysis. IEEE Transactions on Biomedical Engineering, 57(9), 2188–2196.

    Article  Google Scholar 

  • Molla, M. K., & Hirose, K. (2007). Single mixture audio source separation by subspace decomposition of Hilbert spectrum. IEEE Transactions on Audio, Speech and Language Processing, 15(3), 893–900.

    Article  Google Scholar 

  • Ozerov, A., & Fevotte, C. (2010). Multichannel non-negative Matrix factorization in convolutive mixtures for audio source separation. IEEE Transactions on Audio, Speech and Language Processing, 18(3), 550–563.

    Article  Google Scholar 

  • Philipos, C. (2011). Loizou. Speech Quality Assessment, Multimedia Analysis, Processing & Communications, 346, 623–654.

    Article  Google Scholar 

  • Reys, M. J., Ellis, D., & Jojic, N. (2004). Multiband audio modelling for single channel acoustic source separation. In Proceedings of International Conference on Acoustics, Speech, Signal Processing (ICASSP’04) (Vol. 5, pp. 641–644). Montreal, QC.

  • Schmidt, M. N., & Olsson, R. K. (2006). Single channel speech separation using sparse non negative matrix factorization”, In Proceedings of International Conference on Spoken Language Processing (INTERSPEECH), (pp. 2614–2617). Pittsburgh, PA.

  • Schobben, D., Torkkola, K., & Smaragdis, P. (1999). Evaluation of blind signal separation methods. In Proceedings of ICA BSS, Aussois.

  • Stark, Michael, Wohlmayr, Michael, & Pernkopf, Franz. (2011). Source filter based single channel speech separation using pitch information. IEEE Transactions on Audio, Speech and Language Processing, 19(2), 242–254.

    Article  Google Scholar 

  • Tengtrairat, N., Gao, B., & Woo, W. L. (2013). Single channel Blind separation using pseudo stereo mixture and complex 2-D histogram. IEEE Transactions on Neural Networks and Learning Systems, 24(11), 1722–1735.

    Article  Google Scholar 

  • Vincent, E., & Bertin, N. (2014). From Blind to guided audio source separation. IEEE Signal Processing Magazine, 31(3), 107–115.

    Article  Google Scholar 

  • Vincent, E., Gribonval, R., & Fevotte, C. (2006). Performance measurement in blind audio source separation. IEEE Transactions on Speech and Audio Processing., 14(4), 1462–1469.

    Article  Google Scholar 

  • Virtanen, T. (2007). Monaural sound source separation by non negative matrix factorization with temporal continuity and sparseness criteria. IEEE Transactions on Audio, Speech and Language Processing, 15(3), 1066–1074.

    Article  Google Scholar 

  • Wang, Y. H., Yeh, C. H., & Young, H. W. (2014). On the Computational complexity of the empirical mode decomposition algorithm. Physica A: Statistical Mechanics and its Applications, 400(15), 159–167.

    Article  Google Scholar 

  • Wu, K-H., & Chen, C-P., & Yeh, B-F. (2011). Noise-robust speech feature processing with empirical mode decomposition. EURASIP journal on audio, speech and music processing, http://asmp.eurasipjournals.com/content/2011/1/9.

  • Yilmaz, O., & Rickard, S. (2004). Blind separation of speech mixtures via time-frequency masking. IEEE Transactions on Signal Processing, 52(7), 1830–1847.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. BMS College of Engineering, Bangalore, Karnataka, 560019, India

    M. K. Prasanna Kumar

  2. Siddaganga Institute of Technology, Tumkur, Karnataka, 572103, India

    R. Kumaraswamy

Authors
  1. M. K. Prasanna Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Kumaraswamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. K. Prasanna Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prasanna Kumar, M.K., Kumaraswamy, R. Single-channel speech separation using empirical mode decomposition and multi pitch information with estimation of number of speakers. Int J Speech Technol 20, 109–125 (2017). https://doi.org/10.1007/s10772-016-9392-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-016-9392-y

Keywords

Search

Navigation