This thesis explores a single microphone tap localization interface for smartphones - Extended Touch(ET), that detects user-tapped locations on any neighboring surface. The algorithm combines accelerometer and microphone detection making it robust to noise, and does not require knowledge of surface parameters or sensor positioning. It uses acoustic signal as the feature vector and solves for tap inference in two phases - training and detection. The training phase builds a prior-model of the system by storing one or more templates of known tap locations. These templates are used in the detection phase to carry out a k-nearest neighbor classification to detect new tap locations. The algorithm achieves a 92% detection rate on knock taps. A method to detect contiguous tap locations is also proposed.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/42741 |
| Date | 21 November 2013 |
| Creators | Chowdhury, Tusi |
| Contributors | Aarabi, Parham |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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