## 3 Experiments  
Experiment recording conditions, parameters and constraints are kept consistent across the recording sessions to ensure the reliability and repeatability of the results. Before the start of the recording session, all external device notifications, haptic feedback and audio tone are disabled and the device screen is set to remain active during the experiment. We conduct the experiment in quiet room with relatively low ambient noise less than 50 dB. Both the audio samples and movement data are time synchronised and labelled with the touch input location. In total, 150 touch inputs were collected for each of the nine touch input locations, of which 50 were used to train the classifier with the remaining 100 used to test the model. The training dataset was deliberately limited to 50 touch inputs to avoid over-fitting and to demonstrate that a relatively accurate model can be created with limited inputs. Each session is repeated three times on different days in varying room conditions. This allows us to validate our methodology and ensure the results are consistent across each session.  
The movement data and acoustic features extracted from the audio samples are used to distinguish and classify the touch input originating from different locations. To identify the touch input location, a random forest classifier created in Python with scikit-learn [23] is trained on the set of features extracted from the touch input and the results are mapped to one of the nine locations. A random forest classifier with an estimator of 100 trees is selected for this experiment. The choice of the random forest classifier is motivated by the ensemble learning benefits which perturbs-and-combines a number of machine learning models to improve the performance of the classifier. Figure 7 shows the experiment methodology.  
Figure 6: Signal window for left (blue) and right (orange) channels showing how the offset index is applied. (1) The peak is detected, then (2) a sample buffer is applied backwards, which (3) allows us to detect a drop in ZCR, thereby defining the start of the tapping impulse. To ensure we capture the whole impulse event, (4) we begin the window 30% from the start of the ZCR index.  
Several experiments were conducted with the movement data and acoustic features extracted from the audio samples. In the first investigation, named _Movement Data Experiment_, movement data is partitioned into five sets with four sets used to train the classifier and one set reserved for tests. The process is repeated five times and the designated test set is rotated in each iteration. In the second investigation, named _Device Orientation Experiment_, the device is relocated and rotated 90 degrees (Figure 8) between the training and test sessions and the movement data is used to identify the touch input location. In the third investigation, named _Acoustic Feature Experiment_, a classifier is trained to identify the touch input location associated with the audio sample. The full touchscreen surface is tested in the experiment and the device relocated and rotated between training and testing sessions to simulate a realistic user-input scenario. Finally, in the fourth investigation, named _Reduced Touch-Input Area Experiment_, the touch input location is reduced and the classifier is trained to evaluate audio samples from the reduced touch input area under realistic conditions where the device is relocated and rotated between training and testing sessions.