Non-negative matrix factorization on the envelope matrix in cochlear implant

Cochlear implants (CIs) require efficient speech processing to maximize information transfer to the brain, especially in noise. Since speech information in CI is coded in the waveform envelope which is non-negative and is highly correlated to firing of auditory neurons, a novel CI processing strategy is proposed in which sparse constraint non-negative matrix factorization (NMF) is applied to the envelope matrix of 22 frequency channels in order to improve the CI performance in noisy environments. The proposed strategy is evaluated by subjective speech reception threshold (SRT) experiments and subjective quality rating tests at three SNRs. Compared to the default commercially available CI processing strategy, the advanced combination encoder (ACE), the NMF algorithm significantly enhanced speech intelligibility and improved speech quality in the 0 dB and 5 dB for normal hearing subjects with vocoded speech, but not in the 10 dB.

[1]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorization T , 2007 .

[2]  P. Smaragdis,et al.  Non-negative matrix factorization for polyphonic music transcription , 2003, 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684).

[3]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[4]  Liang Chen,et al.  Enhanced sparse speech processing strategy for cochlear implants , 2011, 2011 19th European Signal Processing Conference.

[5]  Patrik O. Hoyer,et al.  Non-negative sparse coding , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[6]  Guoping Li Speech perception in a sparse domain , 2008 .

[7]  Arne Leijon,et al.  A new linear MMSE filter for single channel speech enhancement based on Nonnegative Matrix Factorization , 2011, 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

[8]  R V Shannon,et al.  Speech Recognition with Primarily Temporal Cues , 1995, Science.

[9]  Stefan Bleeck,et al.  Relationship between speech recognition in noise and sparseness , 2012, International journal of audiology.

[10]  Vince D. Calhoun,et al.  Group learning using contrast NMF : Application to functional and structural MRI of schizophrenia , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[11]  Martin Cooke,et al.  A glimpsing model of speech perception in noise. , 2006, The Journal of the Acoustical Society of America.

[12]  Blake S. Wilson,et al.  The Surprising Performance of Present-Day Cochlear Implants , 2007, IEEE Transactions on Biomedical Engineering.

[13]  Andrzej Cichocki,et al.  A Multiplicative Algorithm for Convolutive Non-Negative Matrix Factorization Based on Squared Euclidean Distance , 2009, IEEE Transactions on Signal Processing.

[14]  Bhiksha Raj,et al.  Probabilistic Latent Variable Models as Nonnegative Factorizations , 2008, Comput. Intell. Neurosci..

[15]  Jalil Taghia,et al.  Sparsity level in a non-negative matrix factorization based speech strategy in cochlear implants , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[16]  Andrzej Cichocki,et al.  Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems , 2008, Comput. Intell. Neurosci..

[17]  Stefan J. Mauger,et al.  Clinical Evaluation of Signal-to-Noise Ratio–Based Noise Reduction in Nucleus® Cochlear Implant Recipients , 2011, Ear and hearing.

[18]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[19]  Andrzej Cichocki,et al.  New Algorithms for Non-Negative Matrix Factorization in Applications to Blind Source Separation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[20]  James F Patrick,et al.  The Development of the Nucleus® Freedom™ Cochlear Implant System , 2006, Trends in amplification.

[21]  Nancy Bertin,et al.  Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis , 2009, Neural Computation.

[22]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[23]  Philipos C Loizou,et al.  The intelligibility of speech with "holes" in the spectrum. , 2002, The Journal of the Acoustical Society of America.

[24]  J Bamford,et al.  The BKB (Bamford-Kowal-Bench) sentence lists for partially-hearing children. , 1979, British journal of audiology.

[25]  Michael W. Spratling Learning Image Components for Object Recognition , 2006, J. Mach. Learn. Res..

[26]  M E Lutman,et al.  Speech identification under simulated hearing-aid frequency response characteristics in relation to sensitivity, frequency resolution, and temporal resolution. , 1986, The Journal of the Acoustical Society of America.

[27]  A. M. Mimpen,et al.  Improving the reliability of testing the speech reception threshold for sentences. , 1979, Audiology : official organ of the International Society of Audiology.