1. 1. Denham, S., Coath, M.: The role of form in modeling auditory scene analysis. J. Acoust. Soc. Am. 137(4), 2249–2249 (2015
   2. Vander, G.M., Bourguignon, M., de Beeck, M., Wens, V., Marty, B., Hassid, S., et al.: Left superior temporal gyrus is coupled to
   3. Attended speech in a cocktail-party auditory scene. J. Neurosci.36(5), 1596–1606 (2016)
   4. G. A. Miller and G. A. Heise, “The trill threshold,” J. Acoust. Soc. Amer., vol. 22, pp. 637–638, 1950.
   5. A. S. Bregman, Auditory Scene Analysis. Cambridge, MA, USA: MIT Press, 1990.
   6. G. A. Miller, “The masking of speech,” Psychol. Bull., vol. 44, pp. 105–129, 1947
   7. P. C. Loizou, Speech Enhancement: Theory and Practice, 2nd ed., Boca Raton, FL, USA: CRC Press, 2013.
   8. D. L.Wang and G. J. Brown, Ed., Computational Auditory Scene Analysis: Principles, Algorithms, and Applications.Hoboken,NJ, USA:Wiley,2006.
   9. D. P. Jarrett, E. Habets, and P. A. Naylor, Theory and Applications of Spherical Microphone Array Processing. Zurich, Switzerland: Springer,2016.
   10. D. L. Wang, “Time-frequency masking for speech separation and its potential for hearing aid design,” Trend. Amplif., vol. 12, pp. 332–353, 2008.
   11. G. Hu and D. L.Wang, “Speech segregation based on pitch tracking and amplitude modulation,” in Proc. IEEE Workshop Appl. Signal Process.Audio Acoust., 2001, pp. 79–82.
   12. J. Chen and D. L. Wang, “DNN-based mask estimation for supervised speech separation,” in Audio Source Separation, S. Makino, Ed. Berlin, Germany: Springer, 2018, pp. 207–235.
   13. Rogalsky, C., Poppa, T., Chen, K.H., Anderson, S.W., Damasio,H., Love, T., et al.: Speech repetition as a window on the neurobiology of auditory-motor integration for speech: a voxel-based lesion symptom mapping study. Neuropsychologia 71(01), 18 (2015)
   14. White-Schwoch, T., Davies, E.C., Thompson, E.C., Carr, K.W., Nicol, T., Bradlow, A.R., et al.: Auditory-neurophysiological responses to speech during early childhood: effects of background noise. Hear. Res. 328, 34–47 (2015)
   15. Le´ger, A.C., Reed, C.M., Desloge, J.G., Swaminathan, J., Braida, L.D.: Consonant identification in noise using hilbert-transform Temporal fine-structure speech and recovered-envelope speech for listeners with normal and impaired hearing. J. Acoust. Soc. Am.138(1), 389–403 (2015)
   16. D. L. Wang, “Deep learning reinvents the hearing aid,” IEEE Spectrum, pp. 32–37, Mar. 2017..