# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 4 Experiments
### Experimental Results Analysis  
To evaluate the effectiveness of the proposed AEDCN model, we compare our instrument recognition results on the IRMAS dataset with other instrument recognition models and the results are shown in Table 5.  
Table 5 presents a comparison of various instrument recognition models, including both traditional and neural network-based approaches. The method proposed by [PERSON] et al. utilizes manually extracted features and employs SVM as the classifier. MTF-DNN applies a deep neural network on manually extracted features, while Audio DNN uses a convolutional neural network on music audios for instrument recognition, achieving more effective recognition results compared to traditional methods. ConvNet is another model that designs a CNN on the mel-spectrogram, transforming the CNN to the testing data through max-pooling. Building upon ConvNet, the Multi-task ConvNet introduces an auxiliary classifier to augment labels in the IRMAS dataset, resulting in improved recognition accuracy compared to ConvNet. For data augmentation, WaveGAN ConvNet utilizes WaveGAN to generate training data and performs instrument recognition using ConvNet. Similarly, Voting-Swin-T also generates training data using WaveGAN but adopts a Transformer-based neural network for instrument recognition. Staged trained ConvNet uses a staged training on Melody-solos-DB dataset process for training the ConvNet to recognize instruments in IRMAS dataset. VAE augmentation ConvNet is a ConvNet model which introduces a VAE for data augmentation on Melody-solos-DB dataset.  
According to the results presented in Table 5, the proposed AEDCN model outperforms other neural network-based models as well as data augmentation-based instrument recognition models.  
The AEDCN model consists of three main components: a concatenated two-channel instrument representation, a pre-training process, and an embedded ACEVAE. In the next section, an ablation experiment is conducted to evaluate the effectiveness of these  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1 Micro** & **F1 Macro** \\ \hline SVM [25] & 0.36 & 0.27 \\ [PERSON] et al. [26] & 0.50 & 0.43 \\ MTF-DNN (2018) [27] & 0.32 & 0.28 \\ Audio DNN [28] & 0.55 & 0.51 \\ ConvNet (2017) [15] & 0.62 & 0.52 \\ Muti-task ConvNet (2020) [10] & 0.66 & 0.58 \\ [PERSON] et al. (2021) [14] & 0.65 & 0.55 \\ WaveGAN ConvNet (2021) [7] & 0.65 & 0.60 \\ Voting-Swin-T (2022) [8] & 0.66 & 0.62 \\ Staged trained ConvNet & 0.64 & 0.60 \\ VAE augmentation ConvNet & 0.64 & 0.60 \\ AEDCN & 0.68 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of AEDCN and other instrument recognition models.  
three parts. An ablation experiment involves systematically removing or disabling specific components of a model to assess their impact on performance. By conducting such an experiment, the study aims to analyze how the two-channel instrument representation, a pre-training process, and the embedded ACEVAE contribute to the overall performance of the AEDCN model for instrument recognition. Moreover, we aim to verify the effectiveness of the constructed adversarial loss in the ACEVAE.