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- Creators: Zhou, Yi
- Creators: Department of Speech and Hearing Science
- Creators: Mechanical and Aerospace Engineering Program
- Member of: Theses and Dissertations
- Status: Published
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This report describes the findings of an experiment designed to explore the nature of human hearing using binaural sound. The experiment also set out to determine a way to accurately find positional data from sound. Binaural recordings were made of high frequency sounds at various angles and the data was postprocessed to find the group delay and difference of intensity between the two channels. To do this, two methods were used. The first relied on manually analyzing the data by visually looking for the points of interest. The second method used a MATLAB program to scan the data for the points of interest by using a Fourier analysis. It was determined that while the first method has the potential to provide better results it is impractical and not representative of how human hearing works. The second method was far more efficient and demonstrated the reliance of human hearing on the difference of intensities. It was determined that through the use of the second method accurate positional data could be obtained by comparing the differences with experimental data.
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Experiments were conducted in a sound-attenuated acoustic chamber. Head movement of marmoset monkey was studied under various auditory and visual stimulation conditions. With increasing complexity, these conditions are (1) idle, (2) sound-alone, (3) sound and visual signals, and (4) alert signal by opening and closing of the chamber door. All of these conditions were tested with either house light on or off. Infra-red camera with a frame rate of 90 Hz was used to capture of the head movement of monkeys. To assist the signal detection, two circular markers were attached to the top of monkey head. The data analysis used an image-based marker detection scheme. Images were processed using the Computation Vision Toolbox in Matlab. The markers and their positions were detected using blob detection techniques. Based on the frame-by-frame information of marker positions, the angular position, velocity and acceleration were extracted in horizontal and vertical planes. Adaptive Otsu Thresholding, Kalman filtering and bound setting for marker properties were used to overcome a number of challenges encountered during this analysis, such as finding image segmentation threshold, continuously tracking markers during large head movement, and false alarm detection.
The results show that the blob detection method together with Kalman filtering yielded better performances than other image based techniques like optical flow and SURF features .The median of the maximal head turn in the horizontal plane was in the range of 20 to 70 degrees and the median of the maximal velocity in horizontal plane was in the range of a few hundreds of degrees per second. In comparison, the natural alert signal - door opening and closing - evoked the faster head turns than other stimulus conditions. These results suggest that behaviorally relevant stimulus such as alert signals evoke faster head-turn responses in marmoset monkeys.
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