## 4 Results
### Evaluation Metrics Comparison Among Each Scaled Multi-Spectrogram Settings  
Figure 4 presents a comprehensive comparison of the precision, recall, and F1 score metrics for various instrument recognition models using different configurations of spectrogram features. The x-axis represents the different musical instruments, while the y-axis indicates the metric values. The models compared include configurations such as Log-Mel 128 with Original CST Sizes (Chroma = 12, Spectral Contrast = 7, Tonnetz = 6), Log-Mel 128 with CST Magnified to 1/4 Size (Chroma = 32, Spectral Contrast = 32, Tonnetz = 32), Log-Mel 128 with CST Magnified to 1/2 Size (Chroma = 64, Spectral Contrast = 64, Tonnetz = 64), Log-Mel 128 with CST Magnified to 3/4 Size (Chroma = 96, Spectral Contrast = 96, Tonnetz = 96), and Log-Mel 128 with CST Magnified to Full Size (Chroma = 128, Spectral Contrast = 128, Tonnetz = 128). Each sub-plot within the figure illustrates a specific metric comparison, with the top plot showing precision comparison, the middle plot showing recall comparison, and the bottom plot showing F1 score comparison. In Table 1, the mean precision remains stable across CST sizes, ranging from 0.812 to 0.845, with a consistent standard deviation of 0.12, indicating steady model performance in detecting instruments. Recall shows a slight variation, with mean values between 0.16 and 0.18 and a higher standard deviation for 1/4 and 3/4 sizes (0.14) compared to 1/2 (0.11), indicating variability in detecting specific instruments for certain configurations. The F1 score has a mean range of 0.25 to 0.27, with standard deviations from 0.15 to 0.18, highlighting some variability in balanced performance, especially at the 1/4 and full sizes. Overall, Table 1 suggests that while the 1/4 and full sizes achieve slightly higher F1 scores, they also introduce more variability across instruments. This variability hints that  
Figure 3: Mean average precision (mAP) comparison across various methods on the Open-MIC dataset [31; 32; 33]. certain CST sizes may enhance performance for specific instruments but could reduce consistency across the broader set, indicating a trade-off between targeted accuracy and general stability across CST configurations. Figure 4: Precision, recall, and F1 score comparisons for different spectrogram scaled sizes. OpenMIC-2018 is a multilabel [38] dataset where samples can contain multiple instruments; we use individual confusion matrices for each instrument to evaluate model performance. According to Figures 4 and 5, for instruments like accordion, banjo, bass, drums, guitar, marimba, piano, synthesizer, and trumpet, a high degree of precision is consistently maintained across all configurations, indicating effective differentiation with minimal false positives. However, for cello, clarinet, flute, mandolin, violin, and voice, precision varies, suggesting certain spectrogram features better reduce false positives. Notably, the \"Log-Mel 128 CST Magnified 1/4 Size\" configuration generally provides a balanced performance, capturing essential characteristics effectively. Instruments like cymbals, organs, saxophones, and thrombones exhibit significant precision fluctuations, indicating overlapping features with other instruments, making accurate differentiation more challenging. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Size Setting** & **Precision** & **Std. Precision** & **Mean Recall** & **Std. Recall** & **Mean F1** & **Std. F1** \\ \hline \(\mathbb{10}\) CST Size & 0.845 & 0.12 & 0.18 & 0.14 & 0.27 & 0.18 \\ \(\mathbb{10}\) CST Size & 0.812 & 0.12 & 0.16 & 0.11 & 0.25 & 0.15 \\ \(\mathbb{10}\) CST Size & 0.820 & 0.12 & 0.17 & 0.14 & 0.26 & 0.17 \\ Full Size & 0.827 & 0.12 & 0.17 & 0.14 & 0.26 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and standard deviation of precision, recall, and F1 score for different CST sizes, highlighting performance stability and variability across instruments. Figure 5: Multilabel confusion matrices for the best-performing model on each instrument.