Clinical evaluation of the performance of a blind source separation algorithm combining beamforming and independent component analysis in hearing aid use

There have been several reports on improved blind source separation algorithms that combine beamforming and independent component analysis. However, none of the prior reports verified the clinical efficacy of such combinational algorithms in real hearing aid situations. In the current study, we evaluated the clinical efficacy of such a combinational algorithm using the mean opinion score and speech recognition threshold tests in various types of real-world hearing aid situations involving environmental noise. Parameters of the testing algorithm were adjusted to match the geometric specifications of the real behind-the-ear type hearing aid housing. The study included 15 normal-hearing volunteers and 15 hearing-impaired patients. Experimental results demonstrated that the testing algorithm improved the speech intelligibility of all of the participants in noisy environments, and the clinical efficacy of the combinational algorithm was superior to either the beamforming or independent component analysis algorithms alone. Despite the computational complexity of the testing algorithm, our experimental results and the rapid enhancement of hardware technology indicate that the testing algorithm has the potential to be applied to real hearing aids in the near future, thereby improving the speech intelligibility of hearing-impaired patients in noisy environments.

[1]  Tsuyoki Nishikawa,et al.  Blind source separation combining SIMO-model-based ICA and adaptive beamforming , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[2]  Kostas Kokkinakis,et al.  Using blind source separation techniques to improve speech recognition in bilateral cochlear implant patients. , 2008, The Journal of the Acoustical Society of America.

[3]  Kiyohiro Shikano,et al.  Blind source separation based on a fast-convergence algorithm combining ICA and beamforming , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  David Malah,et al.  Speech enhancement using a minimum mean-square error log-spectral amplitude estimator , 1984, IEEE Trans. Acoust. Speech Signal Process..

[5]  R. Bentler.  Effectiveness of directional microphones and noise reduction schemes in hearing aids: a systematic review of the evidence. , 2005, Journal of the American Academy of Audiology.

[6]  Christopher V. Alvino,et al.  Geometric source separation: merging convolutive source separation with geometric beamforming , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[7]  Kristin J. Van Engen,et al.  Sentence recognition in native- and foreign-language multi-talker background noise. , 2007, The Journal of the Acoustical Society of America.

[8]  Aleksander Sek,et al.  Spatial efficiency of blind source separation based on decorrelation - subjective and objective assessment , 2011, Speech Commun..

[9]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[10]  Jun Yang,et al.  Adaptive Null-Forming Scheme in , 2002 .

[11]  James M Kates,et al.  Effects of Noise, Nonlinear Processing, and Linear Filtering on Perceived Speech Quality , 2010, Ear and hearing.

[12]  Jedrzej Kocinski,et al.  Speech intelligibility improvement using convolutive blind source separation assisted by denoising algorithms , 2008, Speech Commun..

[13]  Qi Lv,et al.  Blind Separation Combined Frequency Invariant Beamforming and ICA for Far-field Broadband Acoustic Signals , 2005, ISNN.

[14]  Junghak Lee,et al.  Reference sound pressure level for Korean speech audiometry , 2011, International journal of audiology.

[15]  Einat Liebenthal,et al.  Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm , 2012, NeuroImage.

[16]  Kazuya Takeda,et al.  Blind source separation combining frequency-domain ICA and beamforming , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[17]  Chaozhu Zhang,et al.  Blind Beamforming Algorithm Based on Improved Complex Value Independent Component Analysis , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[18]  Thomas Sporer,et al.  PEAQ - The ITU Standard for Objective Measurement of Perceived Audio Quality , 2000 .

[19]  Herman J. M. Steeneken,et al.  Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems , 1993, Speech Commun..

[20]  Ho-Young Jung,et al.  Subband-based blind signal separation for noisy speech recognition , 1999 .

[21]  VargaAndrew,et al.  Assessment for automatic speech recognition II , 1993 .

[22]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[23]  Nikolaos Mitianoudis,et al.  Permutation Alignment for Frequency Domain ICA Using Subspace Beamforming Methods , 2004, ICA.

[24]  Paris Smaragdis,et al.  Blind separation of convolved mixtures in the frequency domain , 1998, Neurocomputing.

[25]  Kiyohiro Shikano,et al.  Blind Source Separation Combining Independent Component Analysis and Beamforming , 2003, EURASIP J. Adv. Signal Process..

[26]  Wei Guo,et al.  A blind separation method of instantaneous speech signal via independent components analysis , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[27]  Jen-Tzung Chien,et al.  A new independent component analysis for speech recognition and separation , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[28]  Bruce J Gantz,et al.  Speech recognition in noise for cochlear implant listeners: benefits of residual acoustic hearing. , 2004, The Journal of the Acoustical Society of America.

[29]  Fa-Long Luo,et al.  Adaptive null-forming scheme in digital hearing aids , 2002, IEEE Trans. Signal Process..

[30]  Dani Byrd,et al.  Phonetic analyses of word and segment variation using the TIMIT corpus of American english , 1994, Speech Commun..

[31]  Iván Durán-Díaz,et al.  A two-stage Independent Component Analysis-based method for blind detection in CDMA systems , 2012, Digit. Signal Process..

[32]  Rainer Martin,et al.  COMBINED BEAMFORMING AND FREQUENCY DOMAIN ICA FOR SOURCE SEPARATION , 2006 .

[33]  Manuel Graña,et al.  Face recognition with lattice independent component analysis and extreme learning machines , 2012, Soft Comput..

[34]  Pascal Scalart,et al.  Speech enhancement based on a priori signal to noise estimation , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.