Skip to main content
SpringerLink
Log in

The 2018 Signal Separation Evaluation Campaign

  • Conference paper
  • First Online:
Latent Variable Analysis and Signal Separation (LVA/ICA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10891))

Abstract

This paper reports the organization and results for the 2018 community-based Signal Separation Evaluation Campaign (SiSEC 2018). This year’s edition was focused on audio and pursued the effort towards scaling up and making it easier to prototype audio separation software in an era of machine-learning based systems. For this purpose, we prepared a new music separation database: MUSDB18, featuring close to 10 h of audio. Additionally, open-source software was released to automatically load, process and report performance on MUSDB18. Furthermore, a new official Python version for the BSS Eval toolbox was released, along with reference implementations for three oracle separation methods: ideal binary mask, ideal ratio mask, and multichannel Wiener filter. We finally report the results obtained by the participants.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    sisec.inria.fr.

  2. 2.

    github.com/sigsep/sigsep-mus-oracle.

  3. 3.

    https://sigsep.github.io/musdb.

  4. 4.

    pip install museval.

References

  1. Araki, S., Nesta, F., Vincent, E., Koldovský, Z., Nolte, G., Ziehe, A., Benichoux, A.: The 2011 signal separation evaluation campaign (SiSEC2011): - audio source separation -. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds.) LVA/ICA 2012. LNCS, vol. 7191, pp. 414–422. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28551-6_51

    Chapter  Google Scholar 

  2. Barker, J., Marxer, R., Vincent, E., Watanabe, S.: The third chimespeech separation and recognition challenge: dataset, task and baselines. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 504–511. IEEE (2015)

    Google Scholar 

  3. Barker, J., Vincent, E., Ma, N., Christensen, H., Green, P.: The pascal chime speech separation and recognition challenge. Comput. Speech Lang. 27(3), 621–633 (2013)

    Article  Google Scholar 

  4. Bittner, R., Salamon, J., Tierney, M., Mauch, M., Cannam, C., Bello, J.P.: MedleyDB: a multitrack dataset for annotation-intensive mir research. In: 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, October 2014

    Google Scholar 

  5. Corey, R.M., Singer, A.C.: Underdetermined methods for multichannel audio enhancement with partial preservation of background sources. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 26–30 (2017)

    Google Scholar 

  6. Duong, N.Q.K., Vincent, E., Gribonval, R.: Under-determined reverberant audio source separation using a full-rank spatial covariance model. IEEE Trans. Audio Speech Lang. Process. 18(7), 1830–1840 (2010)

    Article  Google Scholar 

  7. Févotte, C., Gribonval, R., Vincent, E.: Bss_eval toolbox user guide-revision 2.0 (2005)

    Google Scholar 

  8. Fitzgerald, D.: Harmonic/percussive separation using median filtering (2010)

    Google Scholar 

  9. Huang, P.-S., Chen, S.D., Smaragdis, P., Hasegawa-Johnson, M.: Singing-voice separation from monaural recordings using robust principal component analysis. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 57–60. IEEE (2012)

    Google Scholar 

  10. Huang, P.-S., Kim, M., Hasegawa-Johnson, M., Smaragdis, P.: Singing-voice separation from monaural recordings using deep recurrent neural networks. In: ISMIR, pp. 477–482 (2014)

    Google Scholar 

  11. Liu, J.-Y., Yang, Y.-H.: JY Music Source Separtion submission for SiSEC, Research Center for IT Innovation, Academia Sinica, Taiwan (2018). https://github.com/ciaua/MusicSourceSeparation

  12. Liutkus, A., Badeau, R.: Generalized Wiener filtering with fractional power spectrograms. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, QLD, Australia, April 2015

    Google Scholar 

  13. Liutkus, A., Badeau, R., Richard, G.: Low bitrate informed source separation of realistic mixtures. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 66–70. IEEE (2013)

    Google Scholar 

  14. Liutkus, A., Stöter, F.-R., Rafii, Z., Kitamura, D., Rivet, B., Ito, N., Ono, N., Fontecave, J.: The 2016 signal separation evaluation campaign. In: Tichavský, P., Babaie-Zadeh, M., Michel, O.J.J., Thirion-Moreau, N. (eds.) LVA/ICA 2017. LNCS, vol. 10169, pp. 323–332. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53547-0_31

    Chapter  Google Scholar 

  15. Manilow, E., Seetharaman, P., Pishdadian, F., Pardo, B.: NUSSL: the northwestern university source separation library (2018). https://github.com/interactiveaudiolab/nussl

  16. Mimilakis, S.I., Drossos, K., Santos, J., Schuller, G., Virtanen, T., Bengio, Y.: Monaural singing voice separation with skip-filtering connections and recurrent inference of time-frequency mask (2017)

    Google Scholar 

  17. Mimilakis, S.I., Drossos, K., Virtanen, T., Schuller, G.: A recurrent encoder-decoder approach with skip-filtering connections for monaural singing voice separation (2017)

    Google Scholar 

  18. Ono, N., Koldovský, Z., Miyabe, S., Ito, N.: The 2013 signal separation evaluation campaign. In: 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 2013

    Google Scholar 

  19. Ono, N., Rafii, Z., Kitamura, D., Ito, N., Liutkus, A.: The 2015 signal separation evaluation campaign. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds.) LVA/ICA 2015. LNCS, vol. 9237, pp. 387–395. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22482-4_45

    Chapter  Google Scholar 

  20. Rafii, Z., Liutkus, A., Pardo, B.: REPET for background/foreground separation in audio. In: Naik, G.R., Wang, W. (eds.) Blind Source Separation. SCT, pp. 395–411. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55016-4_14

    Chapter  Google Scholar 

  21. Rafii, Z., Liutkus, A., Stter, F.-R., Mimilakis, S.I., Bittner, R.: The MUSDB18 corpus for music separation, December 2017

    Google Scholar 

  22. Rafii, Z., Pardo, B.: Repeating pattern extraction technique (repet): A simple method for music/voice separation. IEEE Trans. Audio Speech Lang. Process. 21(1), 73–84 (2013)

    Article  Google Scholar 

  23. Roma, G., Green, O., Tremblay, P.-A.: Improving single-network single-channel separation of musical audio with convolutional layers. In: International Conference on Latent Variable Analysis and Signal Separation (2018)

    Google Scholar 

  24. Salamon, J., Gómez, E.: Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Trans. Audio Speech Lang. Process. 20(6), 1759–1770 (2012)

    Article  Google Scholar 

  25. Seetharaman, P., Pishdadian, F., Pardo, B.: Music/voice separation using the 2d fourier transform. In: 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 36–40. IEEE (2017)

    Google Scholar 

  26. Takahashi, N., Mitsufuji, Y.: Multi-scale multi-band densenets for audio source separation. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 21–25. IEEE (2017)

    Google Scholar 

  27. Uhlich, S., Giron, F., Mitsufuji, Y.: Deep neural network based instrument extraction from music. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2135–2139. IEEE (2015)

    Google Scholar 

  28. Uhlich, S., Porcu, M., Giron, F., Enenkl, M., Kemp, T., Takahashi, N., Mitsufuji, Y.: Improving music source separation based on deep neural networks through data augmentation and network blending. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 261–265. IEEE (2017)

    Google Scholar 

  29. Vincent, E., Araki, S., Bofill, P.: The 2008 signal separation evaluation campaign: a community-based approach to large-scale evaluation. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds.) ICA 2009. LNCS, vol. 5441, pp. 734–741. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00599-2_92

    Chapter  Google Scholar 

  30. Vincent, E., Araki, S., Theis, F., Nolte, G., Bofill, P., Sawada, H., Ozerov, A., Gowreesunker, V., Lutter, D., Duong, N.Q.K.: The signal separation evaluation campaign (2007–2010): achievements and remaining challenges. Signal Process. 92(8), 1928–1936 (2012)

    Article  Google Scholar 

  31. Vincent, E., Barker, J., Watanabe, S., Roux, J.L., Nesta, F., Matassoni, M.: The second chimespeech separation and recognition challenge: datasets, tasks and baselines. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 126–130. IEEE (2013)

    Google Scholar 

  32. Vincent, E., Gribonval, R., Févotte, C.: Performance measurement in blind audio source separation. IEEE Trans. Audio Speech Lang. Process. 14(4), 1462–1469 (2006)

    Article  Google Scholar 

  33. Vincent, E., Gribonval, R., Plumbley, M.D.: Oracle estimators for the benchmarking of source separation algorithms. Signal Process. 87(8), 1933–1950 (2007)

    Article  Google Scholar 

  34. Vincent, E., Sawada, H., Bofill, P., Makino, S., Rosca, J.P.: First stereo audio source separation evaluation campaign: data, algorithms and results. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 552–559. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74494-8_69

    Chapter  MATH  Google Scholar 

  35. Wang, D.: On ideal binary mask as the computational goal of auditory scene analysis. In: Divenyi, P. (ed.) Speech Separation by Humans and Machines, pp. 181–197. Springer, Boston (2005). https://doi.org/10.1007/0-387-22794-6_12

    Chapter  Google Scholar 

  36. Weninger, F., Hershey, J.R., Roux, J.L., Schuller, B.: Discriminatively trained recurrent neural networks for single-channel speech separation. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 577–581. IEEE (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inria and LIRMM, University of Montpellier, Montpellier, France

    Fabian-Robert Stöter & Antoine Liutkus

  2. NTT Communication Science Laboratories, NTT Corporation, Tokyo, Japan

    Nobutaka Ito

Authors
  1. Fabian-Robert Stöter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antoine Liutkus
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nobutaka Ito
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antoine Liutkus .

Editor information

Editors and Affiliations

  1. Paul Sabatier University, Toulouse, France

    Yannick Deville

  2. Bar-Ilan University, Ramat Gan, Israel

    Sharon Gannot

  3. University of Surrey, Guildford, United Kingdom

    Russell Mason

  4. University of Surrey, Guildford, United Kingdom

    Mark D. Plumbley

  5. University of Surrey, Guildford, United Kingdom

    Dominic Ward

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stöter, FR., Liutkus, A., Ito, N. (2018). The 2018 Signal Separation Evaluation Campaign. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93764-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93763-2

  • Online ISBN: 978-3-319-93764-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation