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Psychophysical Evaluation of Audio Source Separation Methods

  • Andrew J. R. Simpson
  • Gerard Roma
  • Emad M. Grais
  • Russell D. Mason
  • Christopher Hummersone
  • Mark D. Plumbley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)

Abstract

Source separation evaluation is typically a top-down process, starting with perceptual measures which capture fitness-for-purpose and followed by attempts to find physical (objective) measures that are predictive of the perceptual measures. In this paper, we take a contrasting bottom-up approach. We begin with the physical measures provided by the Blind Source Separation Evaluation Toolkit (BSS Eval) and we then look for corresponding perceptual correlates. This approach is known as psychophysics and has the distinct advantage of leading to interpretable, psychophysical models. We obtained perceptual similarity judgments from listeners in two experiments featuring vocal sources within musical mixtures. In the first experiment, listeners compared the overall quality of vocal signals estimated from musical mixtures using a range of competing source separation methods. In a loudness experiment, listeners compared the loudness balance of the competing musical accompaniment and vocal. Our preliminary results provide provisional validation of the psychophysical approach.

Keywords

Deep learning Source separation Perceptual evaluation 

Notes

Acknowledgment

This work was supported by grants EP/L027119/1 and EP/L027119/2 from the UK Engineering and Physical Sciences Research Council (EPSRC). The authors also wish to thank the reviewers for helpful comments on an earlier version of the paper.

References

  1. 1.
    Vincent, E., Gribonval, R., Févotte, C.: Performance measurement in blind audio source separation. IEEE Trans. Audio Speech Lang. Process. 14, 1462–1469 (2006)CrossRefGoogle Scholar
  2. 2.
    Vincent, E., Jafari, M.G., Plumbley. M.D.: Preliminary guidelines for subjective evaluation of audio source separation algorithms. In: Nandi, A.K., Zhu, X., (eds.) Proceedings of ICA Research Network International Workshop, Liverpool, UK, pp. 93–96 (2006)Google Scholar
  3. 3.
    ITU. Recommendation ITU-R BS.1534-3: Method for the subjective assessment of intermediate quality level of audio systems (2014)Google Scholar
  4. 4.
    Emiya, V., Vincent, E., Harlander, N., Hohmann, V.: Subjective and objective quality assessment of audio source separation. IEEE Trans. Audio Speech Lang. Process. 19, 2046–2057 (2011)CrossRefGoogle Scholar
  5. 5.
    Cartwright, M., Pardo, B., Mysore, G.J., Hoffman, M.: Fast and easy crowdsourced perceptual audio evaluation. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 619–623 (2016)Google Scholar
  6. 6.
    Kornycky, J., Gunel, B., Kondoz, A.: Comparison of subjective and objective evaluation methods for audio source separation. In: Meetings on Acoustics, Paris, France, vol. 123, no. 5, p. 3569 (2008)Google Scholar
  7. 7.
    Langjahr, P., Mowlaee, P.: Objective quality assessment of target speaker separation performance in multisource reverberant environment. In: 4th International Workshop on Perceptual Quality of Systems, Vienna, Austria, pp. 89–94 (2013)Google Scholar
  8. 8.
    Gupta, U., Moore, E., Lerch, A.: On the perceptual relevance of objective source separation measures for singing voice separation. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2015) (2015)Google Scholar
  9. 9.
    Cano, E., FitzGerald, D., Brandenburg, K.: Evaluation of quality of sound source separation algorithms: human perception vs quantitative metrics. In: EUSIPCO 2016, pp. 1758–1762 (2016)Google Scholar
  10. 10.
    Fechner, G.T.: Elemente der Psychophysik. Breitkopf und Härtel, Leipzig (1860)Google Scholar
  11. 11.
    Gescheider, G.: Psychophysics: The Fundamentals, 3rd edn. Lawrence Erlbaum Associates, Mahwah (1997)Google Scholar
  12. 12.
    Fletcher, H., Munson, W.A.: Loudness, its definition, measurement and calculation. J. Acoust. Soc. Am. 5, 82–108 (1933)CrossRefGoogle Scholar
  13. 13.
    Moore, B.C.J.: An Introduction to the Psychology of Hearing, 6th edn. Brill, Leiden (2012)Google Scholar
  14. 14.
    Grais, E.M., Roma, G., Simpson, A.J.R., Plumbley, M.D.: Discriminative enhancement for single channel audio source separation using deep neural networks. In: 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA) (2017)Google Scholar
  15. 15.
    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, Heidelberg (2015). doi: 10.1007/978-3-319-22482-4_45 Google Scholar
  16. 16.
    Terrell, M.J., Simpson, A.J.R., Sandler, M.: The mathematics of mixing. J. Audio Eng. Soc. 62(1/2), 4–13 (2014)CrossRefGoogle Scholar
  17. 17.
    Dwass, M.: Modified randomization tests for nonparametric hypotheses. Ann. Math. Stat. 28, 181–187 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Simpson, A.J.R., Roma, G., Grais, E.M., Mason, R.D., Hummersone, C., Liutkus, A., Plumbley, M.D.: Evaluation of audio source separation models using hypothesis-driven non-parametric statistical methods. In: European Signal Processing Conference (EUSIPCO) (2016)Google Scholar
  19. 19.
    Simpson, A.J.R., Roma, G., Plumbley, M.D.: Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network. In: Proceedings of International Conference on Latent Variable Analysis and Signal Separation, pp. 429–436 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrew J. R. Simpson
    • 1
  • Gerard Roma
    • 1
  • Emad M. Grais
    • 1
  • Russell D. Mason
    • 2
  • Christopher Hummersone
    • 2
  • Mark D. Plumbley
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingGuildfordUK
  2. 2.Institute of Sound RecordingUniversity of SurreyGuildfordUK

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