Little is known about the extent to which reverberation affects speech intelligibility by cochlear implant (CI) listeners. Experiment 1 assessed CI users' performance using Institute of Electrical and Electronics Engineers (IEEE) sentences corrupted with varying degrees of reverberation. Reverberation times of 0.30, 0.60, 0.80, and 1.0 s were used. Results indicated that for all subjects tested, speech intelligibility decreased exponentially with an increase in reverberation time. A decaying-exponential model provided an excellent fit to the data. Experiment 2 evaluated (offline) a speech coding strategy for reverberation suppression using a channel-selection criterion based on the signal-to-reverberant ratio (SRR) of individual frequency channels. The SRR reflects implicitly the ratio of the energies of the signal originating from the early (and direct) reflections and the signal originating from the late reflections. Channels with SRR larger than a preset threshold were selected, while channels with SRR smaller than the threshold were zeroed out. Results in a highly reverberant scenario indicated that the proposed strategy led to substantial gains (over 60 percentage points) in speech intelligibility over the subjects' daily strategy. Further analysis indicated that the proposed channel-selection criterion reduces the temporal envelope smearing effects introduced by reverberation and also diminishes the self-masking effects responsible for flattened formants.
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http://dx.doi.org/10.1121/1.3559683 | DOI Listing |
Sci Rep
March 2023
School of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China.
Multi-hop clustering routing protocols are potential solutions to achieve effective and energy-efficient data delivery in cognitive radio sensor networks (CRSNs). Current clustering routing protocols for CRSNs generally assume perfect spectrum sensing, i.e.
View Article and Find Full Text PDFJ Neural Eng
June 2021
School of Automation Science and Engineering, South China University of Technology, Guangzhou, People's Republic of China.
J Neural Eng
June 2021
Beijing Institute of Mechanical Equipment, Beijing, People's Republic of China.
Achieving high precision rapid serial visual presentation (RSVP) task often requires many electrode channels to obtain more information. However, the more channels may contain more redundant information and also lead to its limited practical applications. Therefore, it is necessary to reduce the number of channels to enhance the classification performance and users experience.
View Article and Find Full Text PDFMed Eng Phys
September 2020
School of Computer Science and Technology, Georgia Institute of Technology, 801 Atlantic Dr NW, Atlanta, GA 30332, USA. Electronic address:
Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing to the requirement for active emotion perception. To enhance the performance of electroencephalography (EEG)-based emotional HCI, this paper proposes an improved common spatial pattern combined with a channel-selection strategy (ICSPCS) for EEG-based emotion recognition. Specifically, we first use a common spatial pattern algorithm to design a spatial domain filter according to three different emotions (positive, neutral, and negative).
View Article and Find Full Text PDFSensors (Basel)
July 2017
Graduate Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain-computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger-Procaccia and Higuchi's methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients.
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