Clinical evaluation of signal-to-noise ratio-based noise reduction in Nucleus® cochlear implant recipients

• Published in: Ear and hearing Vol. 32; no. 3; p. 382
• Main Authors: Dawson, Pam W, Mauger, Stefan J, Hersbach, Adam A
• Format: Journal Article
• Language: English
• Published: United States 01.05.2011
• Subjects: Aged; Aged, 80 and over; Algorithms; Cochlear Implants; Electric Stimulation - methods; Hearing Loss - therapy; Humans; Middle Aged; Noise; Perceptual Masking; Phonetics; Speech Intelligibility; Speech Perception
• ISSN: 1538-4667
• DOI: 10.1097/AUD.0b013e318201c200

The aim of this study was to investigate whether a real-time noise reduction algorithm provided speech perception benefit for Cochlear™ Nucleus® cochlear implant recipients in the laboratory. The noise reduction algorithm attenuated masker-dominated channels. It estimated the signal-to-noise ratio of each channel on a short-term basis from a single microphone input, using a recursive minimum statistics method. In this clinical evaluation, the algorithm was implemented in two programs (noise reduction programs 1 [NR1] and 2 [NR2]), which differed in their level of noise reduction. These programs used advanced combination encoder (ACE™) channel selection and were compared with ACE without noise reduction in 13 experienced cochlear implant subjects. An adaptive speech reception threshold (SRT) test provided the signal-to-noise ratio for 50% sentence intelligibility in three different types of noises: speech-weighted, cocktail party, and street-side city noise. In all three noise types, mean SRTs for both NR programs were significantly better than those for ACE. The greatest improvement occurred for speech-weighted noise; the SRT benefit over ACE was 1.77 dB for NR1 and 2.14 dB for NR2. There were no significant differences in speech perception scores between the two NR programs. Subjects reported no degradation in sound quality with the experimental programs. The noise reduction algorithm was successful in improving sentence perception in speech-weighted noise, as well as in more dynamic types of background noise. The algorithm is currently being trialed in a behind-the-ear processor for take-home use.