## 3 Experimental Results
### Database Setup  
We developed a database of three gestures made from four different musical teachers and used their gestures to create a template for comparison in our system. In our experiment, the users made the gestures in a continuous motion, one after another, and the algorithm recognized the gesture as it was being made. A total of 5600 gestures were made. There were 1750 gestures for each basic music gesture and 350 for other gestures that may appear in a performance but are not tempo gestures, such as turning the page or pointing at a section of the orchestra. These gestures were made in a combination of different BPM and facing the camera at different angles. We also gestured to five different BPM for the same interval of time to find the mean squared error and to show the robustness of our system. Finally, we gestured along to three complete songs. Our experimental results are separated into two \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Item** & **Specification** \\ \hline \multirow{3}{*}{Hardware} & \multirow{3}{*}{Personal Computer} & \multirow{3}{*}{CPU: Intel®CoreTM i5-4460 @ 3.2 GHz} \\  & & & \(\bullet\) RAM: 8 GB \\  & & & \(\bullet\) Xbox 360 Kinect \\ \cline{3-3}  & \multirow{3}{*}{Operating System} & \(\bullet\) Microsoft Windows 7 64-bit \\ \cline{3-3}  & & \(\bullet\) Visual Studio C++ 2010 \\ \cline{3-3}  & & \(\bullet\) Kinect for Windows SDK v1.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware and software used in our system. Figure 10: Visual representation of dynamic time warping (DTW) with the optimal warp, where the blue line represents the template and the orange line represents the users’ gesture: (**a**) duple gesture, (**b**) triple gesture, and (**c**) quad gesture. The users had to stand in front of the camera with nothing in between so that the camera could accurately detect where the palms were. The background of the footage and the clothing of the user did not affect the results, because we used a depth camera. Only one user's gestures can be detected at a time, and our system detects each of the user's palms separately. To verify the result, we used half of the data to train the model and another half to verify the accuracy of the model. The accuracy of the musical gesture recognition can be defined as follows [34]:  
\[Accuracy=\frac{number\;of\;correctly\;classified\;data}{number\;of\;testing\;data} \tag{6}\]  
The original images, skeleton images, and the depth maps of the frames of the duple, triple, and quad musical conducting gestures are shown in Figures 14-16, respectively. Each user performed 350 gestures for each musical gesture and 70 gestures for the other category. The accuracy of the experiment is presented in Table 2.  
Figure 14: Screenshots of a single musical conducting gesture (duple) being made while facing the camera, including the 2D video, a skeleton model of the musical conductor, and the depth video. Figure 15: Screenshots of a single musical conducting gesture (triple) being made while facing the camera, including the 2D video, a skeleton model of the musical conductor, and the depth video. Conducting gestures at different speeds affects the accuracy. Table 3 shows the conducting gesture recognition result of different conducting speeds. In the experiment, we used a metronome to assist the user in staying at a constant BPM. The metronome allowed us to determine the exact tempo that the user should be gesturing at, which made the experiment more stable. There were still some misclassifications in our experiment, and one of the main reasons for this is that the speed was too high for the camera to capture the gesture clearly. Our camera could only capture 30 frames per second, which made it difficult to capture a gesture made at 200 BPM, which is approximately 3.33 beats a second. In addition, because the gesture was made so quickly, the movement was much slower to compensate for the quickness, which made it difficult for our system to clearly capture the hand gesture.