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Virtual time to contact (VTC) is a measure of postural stability that estimates the virtual time it would take to reach an individual's stability boundary. This study aimed to validate VTC as measured by a depth sensor and determine whether VTC from the depth sensor distinguishes between older adult fallers and nonfallers compared with a force platform. VTC was assessed in 10 young and 20 older adults by having participants lean in a circular direction followed by 5 balance tests: eyes open, dual task, eyes open foam, eyes closed, and eyes closed foam. Spearman rank correlations and Bland–Altman plots were conducted to determine validity, and receiver operating characteristic curves were constructed to discriminate between fallers and nonfallers. Significant correlations were found in the dual task (P =.03), eyes open foam (P <.01), and eyes closed foam conditions (P =.05). The depth sensor discriminated between fallers and nonfallers in the eyes open (P =.02), dual task (P =.03), and eyes open foam conditions (P =.04). VTC was in agreement between the 2 devices, and VTC derived from a depth sensor can be used to discriminate between older adult fallers and nonfallers during challenging balance conditions. [ABSTRACT FROM AUTHOR]

The localization performance of the source localization algorithms degrades in reverberant conditions. The performance of one such localization algorithm, the localization--extraction (LE) algorithm, was measured systematically as a function of the number of reflecting surfaces in a cubical enclosure. Localization was qualitatively measured using a localization plot and quantized using two objective parameters. A broad-band noise burst and a speech signal were used as stimuli. The degradation of the localization performance was monotonic but not uniform with an increase in the number of reflectors. The performance was found to be proportional to the bandwidth of the stimulus. The performance of the LE algorithm was benchmarked against that of a commonly used signal-subspace technique--multiple signal classification (MUSIC). The LE algorithm was less affected by reflections than the MUSIC algorithm. Degradation of the source localization under high reverberation was found to be more severe at low frequencies, which resulted in the detection of a "phantom" source at 0° for the speech signal. [ABSTRACT FROM AUTHOR]

The reverberation time (RT) measures the persistence of a sound in enclosed acoustic spaces. In a previous work, a method for the blind maximum-likelihood estimation (MLE) of RT using passively received microphone signals was presented. The procedure overcomes the drawbacks of current methods that use a controlled sound source for RT determination. Here, fast algorithms for online implementation of the method are developed. One algorithm, suitable for a one-time determination of the RT, requires O(N) computations for a data frame of length N. A second IIR algorithm, based on Q-levels of quantization, requires O(Q) computations. Results for speech data and choice of algorithms are discussed. [ABSTRACT FROM AUTHOR]