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There is significant variability in the benefit provided by cochlear implants to severely deafened individuals. The reasons why some subjects exhibit low speech recognition scores are unknown; however, underlying physiological or psychophysical factors may be involved. Certain phenomena, such as indiscriminable electrodes and nonmonotonic pitch rankings, might hint at limitations in the ability of individual channels in the cochlear implant and/or sensorineural pathway to convey speech information. In this paper, four approaches for analyzing the results of a simple listening test using speech stimuli are investigated for the purpose of targeting channels of concern in order for follow-on psychophysical experiments to correctly identify channels performing in an 'impaired' or anomalous manner. Listening tests were first conducted with normal-hearing subjects and acoustic models simulating channel-specific anomalies. Results indicate that these proposed analyses perform significantly better than chance in providing information about the location of anomalous channels. Vowel and consonant confusion matrices from six cochlear implant subjects were also analyzed to test the robustness of the proposed analyses to variability intrinsic to cochlear implant data. The current study suggests that confusion matrix analyses have the potential to expedite the identification of impaired channels by providing preliminary information prior to exhaustive psychophysical testing. Index Terms--Cochlear implants, confusion matrix analysis, speech perception.

A two-stage algorithm for landmine detection with a ground penetrating radar (GPR) system is described. First, 3-D data sets are processed using a computationally inexpensive pre-screening algorithm which flags potential locations of interest. These flagged locations are then passed to a feature-based processer which further discriminates target-like anomalies from naturally occurring clutter. Current field trial (over 6500 square meters) and blind test results (over 39000 square meters) are presented and these show at least an order of magnitude improvement over other radar system-based detection algorithms on the same test lanes.

In listening environments with room reverberation and background noise, cochlear implant (CI) users experience substantial difficulties in understanding speech. Because everyday environments have different combinations of reverberation and noise, there is a need to develop algorithms that can mitigate both effects to improve speech intelligibility. Desmond et al. (2014) developed a machine learning approach to mitigate the adverse effects of late reverberant reflections of speech signals by using a classifier to detect and remove affected segments in CI pulse trains. This study aimed to investigate the robustness of the reverberation mitigation algorithm in environments with both reverberation and noise. Sentence recognition tests were conducted in normal hearing listeners using vocoded speech with unmitigated and mitigated reverberant-only or noisy reverberant speech signals, across different reverberation times and noise types. Improvements in speech intelligibility were observed in mitigated reverberant-only conditions. However, mixed results were obtained in the mitigated noisy reverberant conditions as a reduction in speech intelligibility was observed for noise types whose spectra were similar to that of anechoic speech. Based on these results, the focus of future work is to develop a context-dependent approach that activates different mitigation strategies for different acoustic environments.