# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 1 Introduction
This discrepancy can cause inconsistencies between the augmented data and the needs of the instrument recognition model, which can negatively impact its accuracy and performance. Moreover, generating the high-resolution spectrograms brings high computation cost. Therefore, further research is needed to develop more integrated approaches that consider the specific requirements of instrument recognition when performing data augmentation. To address the aforementioned issues, this paper proposes a solution that utilizes the concept of \"tempogram\" for instruments to construct a combined two-channel representation that can better capture the unique rhythm patterns exhibited by different types of instruments. Additionally, the paper introduces a novel instrument recognition strategy called an Augmentation Embedded Deep Convolutional neural Network (AEDCN), which specifically tackles the limitations that arise from independent data augmentation processes. According to References [5; 6], firstly, an AEDCN adds a pre-training process based on introduced datasets as a transfer learning approach to improve the adaptability of the AEDCN for the predominant instrument recognition task. Then, the AEDCN integrates the data augmentation stage as a part of the instrument recognition neural network, briefly, the AEDCN uses two fully connected layers following convolutional layers in the multi-task backbone instrument recognition network and incorporates a constructed Adversarial Conditional Embedded Variational AutoEncoder (ACEVAE) between the added fully connected layers of the multi-task backbone instrument recognition network. To create a generation process with low complexity, the ACEVAE-based data augmentation method is applied to the fully connected layer rather than manipulating the input data. This approach reduces the training difficulty of the generative models while maintaining efficiency. Additionally, an adversarial loss is introduced into the embedded data augmentation model, enabling the generation of more diverse samples based on specific labels. By integrating these techniques, the AEDCN combines data augmentation and label augmentation methods within a unified network architecture to address the challenges associated with insufficient and redundant augmented data. To evaluate the effectiveness of the constructed two-channel representation, the proposed AEDCN and ACEVAE experiments are conducted using the IRMAS dataset. The main contributions of this paper can be summarized as follows:  
1. Proposal of an Augmentation Embedded Deep Convolutional neural Network (AEDCN) for predominant instrument recognition. This approach integrates the data augmentation stage as part of the instrument recognition neural network, addressing the issue of inconsistency between enhanced data and required data. 2. Proposal of an ACEVAE incorporated within the AEDCN architecture. The ACEVAE introduces an adversarial loss into a Conditional Variational AutoEncoder and functions as a data augmentation mechanism and is positioned between the fully connected layer and the output layer of the multi-task backbone instrument recognition network. 3. Proposal of a combined two-channel representation aimed at capturing distinctive rhythm patterns of various instrument types more effectively. This representation comprises a mel-spectrum and a tempogram in the two channels, allowing it to better capture the characteristic rhythmic patterns associated with specific instruments. Moreover, a pre-training process based on introduced datasets for the predominant instrument recognition task is applied. The rest of this paper is organized as follows. Section 2 reviews existing approaches for instrument recognition. Section 3 presents the proposed AEDCN model. Section 4 reports comparative experimental results compared with other commonly used instrument recognition models. Finally, Section 5 concludes and discusses several issues for future work.