John J Galvin1, Qian-Jie Fu, Geraldine Nogaki. 1. Department of Auditory Implants and Perception, House Ear Institute, Los Angeles, California 90057, USA. jgalvin@hei.org
Abstract
OBJECTIVE: While the cochlear implant provides many deaf patients with good speech understanding in quiet, music perception and appreciation with the cochlear implant remains a major challenge for most cochlear implant users. The present study investigated whether a closed-set melodic contour identification (MCI) task could be used to quantify cochlear implant users' ability to recognize musical melodies and whether MCI performance could be improved with moderate auditory training. The present study also compared MCI performance with familiar melody identification (FMI) performance, with and without MCI training. METHODS: For the MCI task, test stimuli were melodic contours composed of 5 notes of equal duration whose frequencies corresponded to musical intervals. The interval between successive notes in each contour was varied between 1 and 5 semitones; the "root note" of the contours was also varied (A3, A4, and A5). Nine distinct musical patterns were generated for each interval and root note condition, resulting in a total of 135 musical contours. The identification of these melodic contours was measured in 11 cochlear implant users. FMI was also evaluated in the same subjects; recognition of 12 familiar melodies was tested with and without rhythm cues. MCI was also trained in 6 subjects, using custom software and melodic contours presented in a different frequency range from that used for testing. RESULTS: Results showed that MCI recognition performance was highly variable among cochlear implant users, ranging from 14% to 91% correct. For most subjects, MCI performance improved as the number of semitones between successive notes was increased; performance was slightly lower for the A3 root note condition. Mean FMI performance was 58% correct when rhythm cues were preserved and 29% correct when rhythm cues were removed. Statistical analyses revealed no significant correlation between MCI performance and FMI performance (with or without rhythmic cues). However, MCI performance was significantly correlated with vowel recognition performance; FMI performance was not correlated with cochlear implant subjects' phoneme recognition performance. Preliminary results also showed that the MCI training improved all subjects' MCI performance; the improved MCI performance also generalized to improved FMI performance. CONCLUSIONS: Preliminary data indicate that the closed-set MCI task is a viable approach toward quantifying an important component of cochlear implant users' music perception. The improvement in MCI performance and generalization to FMI performance with training suggests that MCI training may be useful for improving cochlear implant users' music perception and appreciation; such training may be necessary to properly evaluate patient performance, as acute measures may underestimate the amount of musical information transmitted by the cochlear implant device and received by cochlear implant listeners.
OBJECTIVE: While the cochlear implant provides many deaf patients with good speech understanding in quiet, music perception and appreciation with the cochlear implant remains a major challenge for most cochlear implant users. The present study investigated whether a closed-set melodic contour identification (MCI) task could be used to quantify cochlear implant users' ability to recognize musical melodies and whether MCI performance could be improved with moderate auditory training. The present study also compared MCI performance with familiar melody identification (FMI) performance, with and without MCI training. METHODS: For the MCI task, test stimuli were melodic contours composed of 5 notes of equal duration whose frequencies corresponded to musical intervals. The interval between successive notes in each contour was varied between 1 and 5 semitones; the "root note" of the contours was also varied (A3, A4, and A5). Nine distinct musical patterns were generated for each interval and root note condition, resulting in a total of 135 musical contours. The identification of these melodic contours was measured in 11 cochlear implant users. FMI was also evaluated in the same subjects; recognition of 12 familiar melodies was tested with and without rhythm cues. MCI was also trained in 6 subjects, using custom software and melodic contours presented in a different frequency range from that used for testing. RESULTS: Results showed that MCI recognition performance was highly variable among cochlear implant users, ranging from 14% to 91% correct. For most subjects, MCI performance improved as the number of semitones between successive notes was increased; performance was slightly lower for the A3 root note condition. Mean FMI performance was 58% correct when rhythm cues were preserved and 29% correct when rhythm cues were removed. Statistical analyses revealed no significant correlation between MCI performance and FMI performance (with or without rhythmic cues). However, MCI performance was significantly correlated with vowel recognition performance; FMI performance was not correlated with cochlear implant subjects' phoneme recognition performance. Preliminary results also showed that the MCI training improved all subjects' MCI performance; the improved MCI performance also generalized to improved FMI performance. CONCLUSIONS: Preliminary data indicate that the closed-set MCI task is a viable approach toward quantifying an important component of cochlear implant users' music perception. The improvement in MCI performance and generalization to FMI performance with training suggests that MCI training may be useful for improving cochlear implant users' music perception and appreciation; such training may be necessary to properly evaluate patient performance, as acute measures may underestimate the amount of musical information transmitted by the cochlear implant device and received by cochlear implant listeners.