## 5 Discussion  
We observe that a cross-validated approach using movement data as reported in [9] resulted in good classification accuracy when distinguishing touch input without changing position and orientation. However, its performance severely degrades when device position and orientation are changed (see Section 4.2), conditions which were not explored by [9] and was in fact overlooked in their investigation. As expected, this limits the efficacy of movement data in locating touch input and motivates seeking pathways which generate sensor data that is resistant to changes in device position and orientation. Such a pathway which is more robust can be derived by exploiting multiple microphone signals on the devices. The non-similarity of the upper and lower microphone responses to touch input location shown in Figure 3 and fixed positions relative to the device increase the quality of information extracted, while also ensure less sensitivity to changes in position and orientation. Applying audio processing techniques and analysing the acoustic features therein, we can identify the touchscreen location tapped. This approach will yield increased accuracy with additional microphones, as seen in devices such as the recent iPhone models which now include up to four on-board microphones. Previously, techniques such as Time Difference of Arrival (TDoA) analysis have seen application on standard keyboards [7] and touchscreen surfaces [26] to identify possible sets of keys within a restricted area (virtual keyboard) with up to 90% accuracy [27]. In line with the previous studies, acoustic features used in our setup is able to identify touch input location across the entire touchscreen an average accuracy of 86.2%, offering almost comparable performance. These studies reinforce the fact that physical signals arising from user interaction may offer unintended pathways of data compromise. As introduced in Section 2, MFCC and chroma vectors features contribute over 80% to touch input classification and can used in combination to create an acoustic fingerprint of different sections of the screen to categorize touch input location, by distilling and removing redundancies in the raw signal and allow analysis to be concentrated on salient attributes of the acoustic event. Comparatively, spectral and energy entropy features contribute only up to 20% to categorise user touch input. Filtering out these peripheral features can improve  
Figure 15: Confusion matrix for Reduced Touch Input Area experiment. the generalisability of our model with only minor degradation in accuracy when applying machine learning techniques to determine user touch input location by identifying acoustic markers. The use of acoustic features maintains a good discrimination of touch input classes across varying position and orientations, however it is subject to the maximum distance available between touch inputs location; with a smaller touch input area (numberpad input or simply a smaller device), acoustic features extracted may be less distinct due to the increased proximity of user touch input locations which results in a reduction of accuracy--from 86.2% to 78.8%. Even under such limiting conditions, acoustic features still provide comparable performance with detection techniques that rely on accelerometer data to identify touch input over a larger touch input area, with accuracy ranging from 78.0% on smartphones [4] to 84.6% on smartwatches [28] due to the strictly unique signal response and position of each on-board microphone. This acknowledges the fact that touch input location on touchscreen devices can be retrieved from physical signals associated with the device. It then provides the basis and motivation to further investigate links between the physical characteristics of touch input location, acoustic excitation and how the physical device and signals interact accordingly. To address the threat of unauthorised data access, techniques to limit fine-grained sensor readings can be employed to complicate keystroke inference.