Objective: The goal of the present study was to assess the sound quality of a cochlear implant for single-sided deaf (SSD) patients fit with a cochlear implant (CI).
Background: One of the fundamental, unanswered questions in CI research is "what does an implant sound like?" Conventional CI patients must use the memory of a clean signal, often decades old, to judge the sound quality of their CIs. In contrast, SSD-CI patients can rate the similarity of a clean signal presented to the CI ear and candidate, CI-like signals presented to the ear with normal hearing.
Methods: For Experiment 1 four types of stimuli were created for presentation to the normal hearing ear: noise vocoded signals, sine vocoded signals, frequency shifted, sine vocoded signals and band-pass filtered, natural speech signals. Listeners rated the similarity of these signals to unmodified signals sent to the CI on a scale of 0 to 10 with 10 being a complete match to the CI signal. For Experiment 2 multitrack signal mixing was used to create natural speech signals that varied along multiple dimensions.
Results: In Experiment 1 for eight adult SSD-CI listeners, the best median similarity rating to the sound of the CI for noise vocoded signals was 1.9; for sine vocoded signals 2.9; for frequency upshifted signals, 1.9; and for band pass filtered signals, 5.5. In Experiment 2 for three young listeners, combinations of band pass filtering and spectral smearing lead to ratings of 10.
Conclusion: The sound quality of noise and sine vocoders does not generally correspond to the sound quality of cochlear implants fit to SSD patients. Our preliminary conclusion is that natural speech signals that have been muffled to one degree or another by band pass filtering and/or spectral smearing provide a close, but incomplete, match to CI sound quality for some patients.
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http://dx.doi.org/10.1097/MAO.0000000000001449 | DOI Listing |
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Department of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur- 603203, Chengalpattu, Tamil Nadu, India.
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Department of Applied Social Sciences, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
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College of Computer Science and Technology, Xinjiang University, Urumqi, Xinjiang, China.
In speech signal processing, time-frequency analysis is commonly employed to extract the spectrogram of speech signals. While many algorithms exist to achieve this with high-quality results, they often lack the flexibility to adjust the resolution of the extracted spectrograms. However, applications such as speech recognition and speech separation frequently require spectrograms of varying resolutions.
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