Affiliations: [a] Lyon Neuroscience Research Center, PACS Team, Lyon, France | [b] University Lyon 1, Lyon, France | [c] Neurelec, 2720 Chemin Saint-Bernard, Vallauris, France | [d] Audiology and ENT Department, Edouard Herriot Hospital, Lyon, France
Correspondence:
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Corresponding author: Arnaud Jeanvoine, INSERM U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Equipe PACS, Hôpital Edouard Herriot, Pavillon U, 5 Place d'Arsonval, F-69437 Lyon cedex 03, France. Tel.: +33 4 72 11 05 03; Fax: +33 4 72 11 05 04; E-mail:arnaudjeanvoine@yahoo.fr
Abstract: French phonemes perception in noisy conditions, in the case of a Binaural
Cochlear Implant (BCI) coding, is seen in the present study. In the current
work, the action of binaural noise reduction algorithms is investigated,
through the use of a vocoder simulation with normal hearing listeners. Three binaural noise reduction algorithms, used in classical hearing aids,
have been considered: beamformer, Doerbecker algorithm combined with Ephraim
and Malah noise estimator and Doerbecker algorithm combined with Scalart
noise estimator. Then a cochlear implant (CI) coding (bins grouped into
frequency bands) transformed the signal at the end of the processing chain.
Also, a percentage of the input signal was ``re-injected'' (added)
before CI coding. Twenty-six normal hearing subjects participated in the experiment and they
listened to sessions including 3 signal-to-noise ratios, 3 re-injection
coefficients; they evaluated the coded signal (phoneme recognition). Then, a
noise was added to jam the signal. The noise came from five different noise
angles and the speech was issued from the front (zero deg azimuth).
Altogether, experimental sessions tested 150 conditions.
Best results were obtained using the beamformer algorithm. Doerbecker with
Ephraim and Malah estimator led to good results; this strategy was more
efficient than the Doerbecker with Scalart estimator. Results were more
sensitive to the speech processing strategy than to the noise angle.
Re-injection of the input signal improved the recognition. In this BCI
coding environment, noise reduction algorithms led to an improvement of
20% in phoneme recognition.
Keywords: Binaural signal processing, noise reduction algorithms, Binaural Cochlear Implant, vocoder coding, front target, noise angle, French phonemes recognition
