Acoustic feedback suppression in binaural hearing aids

  • Fatmah Tarrab Mail Department of Biomedical Engineering, Faculty of Electrical and Mechanical Engineering, Damascus University, Damascus, Syria
  • Zouheir Marmar Department of Biomedical Engineering, Faculty of Electrical and Mechanical Engineering, Damascus University, Damascus, Syria
  • Isam Alamine Department of Otorhinolaryngology, Faculty of Medicine, Damascus University, Damascus, Syria
Source separation, computational auditory scene analysis, acoustic feedback, hearing aids


Background and Aim: The hearing loss and acoustic feedback signal in the binaural hearing aids may annoy users, make speech reorganization difficult, and affect the sound quality. This paper presents a new method for acoustic feedback suppression in binaural hearing aids, based on the combination of binaural information and blind source separation. This method can enhance sound from the specific direction of the user and cancels acoustic feedback signal so that the user can hear clearly.
Methods: In the proposed method, the binaural information (interaural time differences (ITDs) and interaural level differences (ILDs)) was generated using computational auditory scene analysis (CASA). In addition, we used underdetermined blind source separation (BSS) for the automatic classification of the time-frequency (T-F) units of the speech mixture spectrogram. The system performance was evaluated using 32 acoustic English speech mixtures. The sound quality was assessed using 19 normal-hearing listeners of both genders.
Results: The system achieved a good acoustic feedback suppression performance by giving a higher signal to noise ratio and 18.07 dB on average for the signal-to-feedback ratio. In addition, the sound after processing had a high quality according to the subjective assessment.
Conclusion: Our system allowed the user to increase the gain of the hearing aid without affecting the sound quality.


1. Avan P, Giraudet F, Büki B. Importance of binaural hearing. Audiol Neurootol. 2015;20 Suppl 1:3-6.
2. Dillon H. Hearing aids. 2nd ed. London: Thieme Medical Pub; 2001.
3. Guo M, Jensen SH, Jesper J. Evaluation of state-of-the-art acoustic feedback cancellation systems for hearing aids. J Audio Eng Soc. 2013;61(3):125-37.
4. Lee HW, Jeon MY. A combined feedback and noise cancellation algorithm for binaural hearing aids. Advances in Electrical and Computer Engineering. 2011;11(3):35-40.
5. Chisaki Y, Matsuo K, Usagawa T. Howling canceler using interaural level difference for binaural hearing assistant system. Acoust. Sci. & Tech. 2007;28(2):90-7.
6. Lombard A, Reindl K, Kellermann W. Combination of adaptive feedback cancellation and binaural adaptive filtering in hearing aids. EURASIP J Adv Signal Process. 2009;2009:1-15.
7. Spriet A, Rombouts G, Moonen M, Wouters J. Adaptive feedback cancellation in hearing aids. Journal of the Franklin Institute. 2006;343(6):545-73.
8. Boukis C, Mandic DP, Constantinides AG. Toward bias minimization in acoustic feedback cancellation systems. J Acoust Soc Am. 2007;121(3):1529-37.
9. Shao Y, Srinivasan S, Jin Z, Wang D. A computational auditory scene analysis system for speech segregation and robust speech recognition. Comput Speech Lang. 2010;24(1):77-93.
10. May T, van de Par S, Kohlrausch A. A probabilistic model for robust localization based on a binaural auditory front-end. IEEE Trans Audio Speech Lang Process. 2011;19(1):1-13.
11. Sawada H, Araki S, Makino S. Underdetermined convolutive blind source separation via frequency bin-wise clustering and permutation alignment. IEEE Trans Audio Speech Lang Process. 2011;9(3):516-27.
12. In: Ganesh RN, Wang W, editors. Blind source separa¬tion: advances in theory, algorithms and applications. Berlin: Springer; 2014.
13. Trahiotis C, Bernstein LR, Stern RM, Buell TN. Interaural correlation as the basis of a working model of binaural processing: an introduction. In: Popper AN, Fay RR, editors. Sound Source Localization.1st ed. New York: Springer; 2005. p. 238-71.
14. Makino S, Sawada H, Mukai R, Arki S. Blind source separation of convolutive mixtures of speech in frequency domain. IEICE Trans Fundamentals. 2005;E88-A(7):1640-55.
15. Garofolo JS, Lamel LF, Fisher WM, Fiscus JG, Pallett DS, Dahlgren NL, et al. TIMIT acoustic-phonetic continuous speech corpus LDC93S1. Web Download. Philadelphia: Linguistic Data Consortium, 1993.
16. Jafari I, Haque S, Togneri R, Nordholm S. Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking. EURASIP J Adv Signal Process. 2013;2013(1):162.
17. Vincent E, Sawada H, Bofill P, Makino S, Rosca JP. First stereo audio source separation evaluation campaign: data, algorithms and results. Proceedings of the 7th International Conference on Independent Component Analysis and Signal Separation; 2007 Sep 9; London, UK. Berlin: Springer.
18. Viswanathan M, Viswanathan M. Measuring speech quality for text-to-speech systems: development and assessment of a modified mean opinion score (MOS) scale. Comput Speech Lang. 2005;19(1):55-83.
How to Cite
Tarrab F, Marmar Z, Alamine I. Acoustic feedback suppression in binaural hearing aids. Aud Vestib Res. 25(4):207-214.
Research Article(s)