A rise in interest for service robotic rovers produces a need for a low-cost method for source localization in order for a prospective robotic unit to engage with a human operator. This study examines the use of the LMS algorithm for constructing a beamformer using an optimized Weiner filter solution for this source localization application and evaluates the robustness of a developed characterization method for assuring that a proper approximation for the desired signal is achieved. The method presented in this paper encompasses using a filter and sum method in which the sums are generated for a selected set of filter angles, and this set of sums are compared and characterized to produce a selection for an approximate arrival angle from the sound source to the microphone array. These filters are adaptively trained offline using a generated desired signal chirp to represent the average human whistle and a training data set for each of the four possible room configurations. This method was tested to determine if a selected filter configuration could still produce viable outputs for scenarios in which the testing room had been changed, whether noise was injected into the testing environment, if two or three microphones were used in testing process, and whether the filter angles are aligned with the arrival angles of the signal. Results on the robustness of the adaptive LMS beamformer are presented. Limitations of the system performance are discussed and possible solutions for results that have undesired performance are given in future work. / Master of Science / A rise in interest for service robotic rovers produces a need for a low-cost method for locating a sound source so that a potential service robot can interact with a human operator. In this study, a beamformer is implemented to approximate a direction for the sound source. This beamformer is comprised of a set of trained filters for the designed microphone array. These filters were trained based on three training conditions of training room, the number of microphones used, and whether additive or ambient noise is used during training. The training signal for the filters consisted of a chirp from 1 to 2.5 kHz to mimic a portion of the human whistling spectrum. Once trained, these beamformers were then given data from separate tests to determine if a distinct and correct approximation could be determined. This paper suggests a method to use the correlation of each beamformer to the training signal to determine both the maximum correlated beamformer and whether correlation is distinct from greater than the other beamformers examined. These results are finally examined under an ANOVA and percent difference process to determine if the three training conditions improve the average prediction of the angle of arrival of the source signal for the generated beamformers.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/84418 |
| Date | 26 July 2018 |
| Creators | Coleman, Thomas Christopher |
| Contributors | Mechanical Engineering, Southward, Steve C., Wicks, Alfred L., Patterson, Cameron D. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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