## 4 Experiments
### Ablation Experiment  
The MMGAT architecture integrates three core innovations: an instance correlation graph for modeling polyphonic interaction patterns, label semantic embeddings, and a multi-scale graph attention network to hierarchically aggregate spectral-temporal features. In this ablation experiment, we first discuss the role of each component, followed by a discussion on the similarity metrics used in the experiments, and finally, we conduct a visualization analysis of the features obtained by MMGAT. MMGAT defined on mel-spectrogram is denoted as MMGAT-mel-spectrogram (the same 158-dimensional features with the input of Reference [10]), and the MMGAT without multi-scale attention is named MMGAT-single. For comparison, we also use MMGAT on the constructed instance correlation graph without using auxiliary classification in the experiments; this model is denoted as MMGAT-principal. MMGAT without using the label semantic embeddings is denoted as MMGAT_graph. MMGAT without the center loss is denoted as MMGAT_without_center. The results are shown in Table 5.  
As Table 6 shows, the constructed instance correlation graph, the introduced label semantic embeddings, and the proposed instance-based multi-scale graph attention neural network of MMGAT are effective. Furthermore, we verify that the constructed similarity measure in the instance correlation graph is effective for the instrument recognition task in Table 6.  
Figure 2: A heatmap for the OpenMIC-IRMAS dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1 Micro** & **F1 Macro** \\ \hline MMGAT-mel-spectrogram & 0.60 & 0.60 \\ MMGAT-single. & 0.62 & 0.60 \\ MMGAT-principal & 0.62 & 0.60 \\ MMGAT\_without\_center & 0.62 & 0.61 \\ MMGAT\_graph & 0.62 & 0.62 \\ MMGAT & 0.63 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation experiment of MMGAT on different models.  
As shown in Table 6, MMGAT-Euc is an MMGAT model that uses Euclidean distance for constructing the instance correlation graph, MMGAT-L1 is an MMGAT model that uses the L1 distance function for constructing the instance correlation graph, and MMGAT-EMD is an MMGAT model that uses the Earth Mover's Distance function for constructing the instance correlation graph. As Table 6 shows, cosine similarity reaches higher performances than the other commonly used similarity measures.  
To further understand the recognition results of MMGAT, we conduct a visual analysis based on t-distributed stochastic neighbor embedding in Figure 3.  
This figure shows a visualization of the features obtained from layer L of MMGAT, prior to the classification layer, after dimensionality reduction. First, we extracted the features corresponding to a batch of samples from layer L. However, the feature dimensions are quite high, making it difficult to visualize directly, so we used the t-SNE method. We employ the t-distributed stochastic neighbor embedding (t-SNE) [29] algorithm, a popular technique for reducing the dimensionality of high-dimensional data, to visualize the extracted features. The t-SNE method reveals that the features can be effectively clustered into 20 distinct classes in a two-dimensional space. This outcome suggests that the proposed MMGAT model is capable of generating relatively clear classification  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **F1 Micro** & **F1 Macro** \\ \hline MMGAT-Euc & 0.60 & 0.60 \\ MMGAT-L1 & 0. 61 & 0.59 \\ MMGAT-EMD & 0.62 & 0.61 \\ MMGAT & 0.63 & 0.62 \\ \hline \end{tabular}
\end{table}
Table 6: The ablation experiment different similarity measures in MMGAT.  
Figure 3: A mini-batch feature visual analysis of generated clusters from MMGAT and AEDCN.  
boundaries. As illustrated in Figure 2, the clusters produced by MMGAT exhibit greater separation compared to the other models.