The temporal fine structure (TFS) of acoustical signals, represented as the phase-locking pattern of the auditory nerve, is the major information for listeners performing a variety of auditory tasks, e.g., judging pitch and detecting interaural time differences (ITDs). Two experiments tested the hypothesis that processes for TFS-based pitch and ITD involve a common mechanism that processes TFS information and the efficiency of the common mechanism determines the performance of the two tasks. The first experiment measured the thresholds for detecting TFS-based pitch shifts (Moore and Moore, J Acoust Soc Am 113:977-985, 2003) and for detecting ITD for a group of normal-hearing listeners. The detection thresholds for level increments and for interaural level differences were also measured. The stimulus was a harmonic complex (F0 = 100 Hz) that was spectrally shaped for the frequency region around the 11th harmonic. We expected a positive correlation between the pitch and ITD thresholds, based on the hypothesis that a common TFS mechanism plays a determinant role. We failed to find evidence for a positive correlation, hence no support for the above hypothesis. The second experiment examined whether perceptual learning with respect to detecting TFS-based pitch shifts via training would transfer to performance in other untrained tasks. The stimuli and tasks were the same as those used in the first experiment. Generally, training in the pitch task improved performance in the (trained) pitch task, but degraded the performance in the (untrained) ITD task, which was unexpected on the basis of the hypothesis. No training effect was observed in the other untrained tasks. The results imply that the pitch and ITD processes compete with each other for limited neural resources.

Quantification of human gait with sensors has enormous potential in health and rehabilitation applications. Objective measurement of gait features in the home and community can reveal the true nature of impact of disease on activities of daily living or response to interventions. Previously reported gait event detection methods have achieved good success, yet can produce errors in some irregular gait patterns. In this paper, we propose a novel unsupervised detection of gait events and gait duration by combining two exclusive processes: (i) exploration of gait event candidates based on iterative running of existing methods with changing parameters and, (ii) selection of the candidate which satisfies gait-specific biomechanical restrictions (e.g., when one leg is in swing, another leg is likely to be in stance). We evaluated this approach using data from a single-axis gyroscope on the left and right ankles in three experimental conditions. The proposed method decreased the timing error for detection of gait events (toe off and heel strike) in irregular gait patterns compared with the conventional method. It also improved the accuracy of measurement of gait duration in a longitudinal free-living dataset and distinguishing gait from non-gait actions.

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