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Extended touch user interfaces

This paper explores an extended touch-based input interface for mobile devices by inferring the user-tapped location on any neighboring surface. We propose the feasibility of achieving a portable solution by using only the device microphone, without needing multiple sensors or highly specialized single piezoelectric sensor as has been done in past research. Training and classification of discrete tap locations is done using cross-correlation analysis on the audio feature vector. We first explore a binary classifier that performs pair-wise detection of taps, which results in a 92% detection rate across different surfaces. The solution is extended to multi-tap detection using a K-Nearest Neighbor (kNN) algorithm trained on 17 output classes, resulting in 92% knuckle tap and 63% soft tap detection.