## 4 Experiments and Results
### Note Extraction Using Signal Processing  
In order to extract the notes, we had to obtain a captured signal and, so, the waveform signal was captured. Both DFT and HPS were applied to the waveform, generating two different power spectra to capture the note frequencies. Figures 9 and 10 show the waveform and power spectrum of the C4 note, captured by recording a note played on the guitar. The power spectrum shows and breaks down each frequency which composes a note. Table 3 shows the results for each frequency detected using the HPS and DFT approaches. Each has two columns: the detected frequency and the closest frequency (i.e., rounded to classify each note). The standard frequency column resembles the benchmark used for each note label. The measures used to determine the performance of these approaches were the mean squared error (MSE) and the accuracy:  
\[MSE=1/N\sum_{i=0}^{N}(Yi-\widetilde{Y}i)^{2}, \tag{3}\]  
where \(N\) is the total number of notes on the guitar, \(Yi\) is the standard benchmark sample, and \(\widetilde{Y}i\) is the detected frequency; and  
\[Efficiency=\frac{correctSamples}{NoSamples}\times 100, \tag{4}\]  
where the correct samples are the number of samples from the closest Freq. column matching the standard frequency in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Size** & **1.5 GB** \\ \hline Number of rows or frames & 12,607 rows \\ Number of features & 68 \\ Duration & from 2 to 30 s \\ Number of videos & 40 \\ Number of classes & 2 \\ Number of incorrect classes & 20 \\ Number of correct classes & 20 \\ Guitar type & Spanish classic guitar \\ Camera for recording & Xiaomi note 8 pro \\ \hline \end{tabular}
\end{table}
Table 2: Classical guitar recordings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DFT**} & \multicolumn{3}{c}{**HPS**} \\ \hline
**Note** & \begin{tabular}{c} **Detected** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Closest** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Detected** \\ **Frequency** \\ \end{tabular} & \
\begin{tabular}{c} **Closest** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Frequency** \\ \end{tabular} \\ \hline C3 & 262 & 261 & 131 & 130 & 130 \\ C4 & 262 & 261 & 262 & 261 & 261 \\ C5 & 525 & 523 & 525 & 523 & 523 \\ C\(\sharp\)3 & 282 & 277 & 140 & 138 & 138 \\ C\(\sharp\)4 & 278 & 277 & 278 & 277 & 277 \\ C\(\sharp\)5 & 557 & 554 & 556 Signal Processing Results  
To calculate the accuracy of each equation, we took the percentage of all matching rows from the closest frequency and standard frequency columns. Furthermore, we calculated the mean square error between the standard frequency column and the detected frequency column, which allowed us to calculate how much the detected frequency deviated from the standard benchmark. HPS clearly outperformed DFT, in terms of both accuracy and MSE, as HPS obtained an accuracy of 95% and a MSE of 1402. On the other hand, DFT obtained an accuracy of 65% and an MSE of 39198, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DFT**} & \multicolumn{3}{c}{**HPS**} \\ \hline
**Note** & \begin{tabular}{c} **Detected** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Closest** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Detected** \\ **Frequency** \\ \end{tabular} & \
\begin{tabular}{c} **Closest** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Frequency** \\ \end{tabular} \\ \hline D\(\sharp\)3 & 156 & 155 & 156 & 155 & 155 \\ D\(\sharp\)4 & 312 & 311 & 311 & 311 & 311 \\ D\(\sharp\)5 & 625