# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 4 Experiments
### Data Set  
In this paper, we use the IRMAS dataset as our dataset. It should be noted that the training set of IRMAS is single-labelled and the testing set of IRMAS is multi-labelled. Music audio segments in the IRMAS dataset [23] are stereo with a 44.1 KHz sampling rate. These music segments are played in different styles. The recordings come from decades, and thus they have different recording qualities. The purpose of IRMAS is to recognize instruments in audio segments, including cello, clarinet, flute, acoustic guitar, electric guitar, organ, piano, saxophone, trumpet, violin, and human voice. In experiments, IRMAS is divided into the training set and the testing set. We use the training set in IRMAS to train the network and use 15% of the training as the validation set. For the IRMAS dataset, the attributes of the dataset are shown in Table 3. For pre-training, we add a dataset to improve the adaptability of AEDCN for predominant instrument recognition; as the main IRMAS dataset contains monophonic training data and polyphonic testing data, we use a monophonic melody-solos-DB dataset [24] to improve the recognition ability in the training process and keep consistency with the task of predominant instrument recognition on IRMAS dataset.  
As Table 3 shows, the dataset is imbalanced and we generate designated classes of instruments in the training process by using the embedded ACEVAE. We generate fake music audios to the maximum number of the original dataset. Moreover, the auxiliary classifier contains three classes, the relation of the auxiliary classes and principal classes are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instruments** & **Principal Classes** & **Auxiliary Classes** \\ \hline Cello & cel & Soft onset \\ Clarinet & cla & Soft onset \\ Flute & flu & Soft onset \\ Acoustic guitar & gac & Hard onset \\ Electric guitar & gel & Hard onset \\ Organ & org & Hard onset \\ Piano & pia & Hard onset \\ Saxophone & sax & Soft onset \\ Trumpet & tru & Soft onset \\ Violin & vio & Soft onset \\ Voice & voi & Other \\ \hline \hline \end{tabular}
\end{table}
Table 4: The relation of the auxiliary classes and principal classes in IRMAS [10].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instruments** & **Abbreviations** & **Training** & **Testing** \\ \hline Cello & cel & 388 & 111 \\ Clarinet & cla & 505 & 62 \\ Flute & flu & 451 & 163 \\ Acoustic guitar & gac & 637 & 535 \\ Electric guitar & gel & 760 & 942 \\ Organ & org & 682 & 361 \\ Piano & pia & 721 & 995 \\ Saxophone & sax & 626 & 326 \\ Trumpet & tru & 577 & 167 \\ Violin & vio & 580 & 211 \\ Voice & voi & 778 & 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Instruments in IRMAS [13].  
As Table 4 shows, the auxiliary classifier faces more coarse-grained classes than the principal classifier. To evaluate the performance of the proposed AEDCN, we calculate the micro average and macro average of precision, recall, and \(F1\) score. These indicators are calculated as follows:  
\[\begin{array}{ll}F1_{micro}=\frac{2p_{micro}R_{micro}}{\frac{p_{micro}R_{micro} }{p_{micro}+R_{micro}}},&F1_{macro}=\frac{2p_{macro}R_{micro}}{P_{macro}+R_{ macro}}\\ P_{micro}=\frac{\sum_{l=1}^{L}l_{l}}{\sum_{l=1}^{L}(p_{l}+f_{lp})},&R_{micro}= \frac{\sum_{l=1}^{L}l_{l}}{\sum_{l=1}^{L}((p_{l}+f_{lp})}\\ P_{macro}=\frac{1}{L}\sum_{l=1}^{L}\frac{lp_{l}}{(lp_{l}+f_{lp})},&R_{macro}= \frac{1}{L}\sum_{l=1}^{L}\frac{lp_{l}}{(lp_{l}+f_{lp})}\end{array}\]  
where \(L\) is the number of classes. \(l\) is the label index. \(tp_{l}\) is true positive. \(f_{lp}\) is false positive, and \(f_{lp}\) is false negative for each label.