# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 1 Introduction  
Instrument recognition plays a crucial role in Music Information Retrieval, particularly in identifying the predominant instruments in a piece of music. In recent years, machine learning-based approaches have gained prominence in this field. Traditionally, acoustic features such as Zero-crossing rate, Spectral centroid, Mel-spectrogram, or MFCC have been extracted from music signals for instrument recognition. These features are then used with classifiers like Naive Bayes or SVM [1; 2; 3]. However, these traditional machine learning-based methods have limitations in terms of recognition capability. They often struggle to capture the intricate characteristics and variations in different musical instruments, resulting in suboptimal performance. Moreover, these methods heavily rely on handcrafted features, which may not fully encompass the complex nature of musical instrument sounds. In recent years, deep learning techniques, particularly deep neural networks, have emerged as a dominant approach for instrument recognition. Deep neural networks can automatically learn and extract discriminative features from raw audio data, allowing formore robust and accurate instrument recognition. These models are capable of handling the complexities and variations inherent in musical instrument sounds, resulting in improved performance. Researchers have made significant advancements in instrument recognition by leveraging the power of deep learning. However, directly applying neural networks to music audio signals may struggle to effectively differentiate instruments in music with varying recording qualities. To address this issue and improve recognition performance, researchers convert 1-dimensional music signals into two-dimensional spectrograms. They then utilize two-dimensional convolutional neural networks and transformers on these spectrograms for instrument recognition [4]. These spectrogram-based methods have shown more effective recognition results compared to traditional methods and other deep neural networks based on music audio signals alone. Moreover, some transfer learning-based methods and multi-instance multi-label-based methods are used for instrument recognition. However, instrument recognition methods still face challenges when dealing with low-quality recordings, especially in polyphonic music. To address the challenges posed by low recording quality, researchers firstly pre-train their models by using supplemental music datasets and then train their models with the target datasets [5; 6]. Moreover, researchers also introduce data augmentation methods into instrument recognition models. One approach is the proposal of two-stage instrument recognition methods. In the first stage, data augmentation techniques like Generative Adversarial Networks (GANs) are utilized to generate additional training data. In the second stage, deep neural networks such as Convolutional Neural Networks (CNNs), Gate Recurrent Units (GRUs), Convolutional Gate Recurrent Units (Conv GRUs), and Transformers are employed to map the augmented data to instrument labels [7; 8; 9]. Additionally, researchers have explored label augmentation, which involves constructing auxiliary classifiers based on additional labels introduced within a multi-task learning framework. By incorporating label augmentation, a more effective instrument recognition CNN can be built [10]. These data augmentation-based methods alleviate the challenges caused by low recording quality and contribute to improvements in music instrument recognition effectiveness. Current instrument recognition models are typically designed based on two-dimensional spectrograms, which may not capture the full expressive range of musical instruments, and the corresponding data augmentation processes are usually independent of the instrument recognition process. In particular, the types of instrument spectrograms generated through data augmentation may not necessarily align with the specific requirements of the instrument recognition models.