# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 2 Related Works  
### Instrument Recognition  
In the field of instrument recognition, there has been a growing interest in machine learning-based methods. Several approaches incorporating different features and algorithms have been proposed by researchers. [PERSON] et al. [11] utilized mel-frequency cepstral coefficients (MFCCs) and a Principal Component Analysis (PCA) to model instrument features. They applied these techniques for instrument recognition tasks. [PERSON] et al. [12] introduced a cepstrum representation method called unified discrete cepstrum (UDC), along with its mel-scale variant MUDC. These representations were combined with a radial basis function (RBF) kernel support vector machine (SVM) for instrument recognition. [PERSON] et al. [13] focused on using the spectral peak of the fundamental frequency of the harmonic sequence as a feature representation for identifying musical instruments, especially in the presence of background music.  
With the advancements in deep learning, deep neural networks have also been widely applied to instrument recognition tasks. [PERSON] et al. [14] proposed an approach to detect the activity of musical instruments in polyphonic music. They trained a deep neural network using one-second audio clips and employed temporal max-pooling aggregation to calculate the final prediction score. To further enhance instrument recognition, researchers have explored the use of image processing techniques by converting one-dimensional music signals into two-dimensional spectrograms. [PERSON] et al. [15] adopted a CNN structure for instrument recognition, using the mel-frequency spectrogram as the input. They also incorporated Convolutional Gated Recurrent Units (Conv GRU) for better performance. However, these methods often face challenges, particularly when dealing with low-qualityrecordings or polyphonic music. To address this issue, data augmentation methods have been introduced in instrument recognition models. [PERSON] et al. [10] proposed constructing a network with an auxiliary classification based on onset groups and instrument families to generate valuable training data. In another study by using convolutional recurrent neural networks (CRNN), predominant instrument recognition in polyphonic music was addressed [9]. [PERSON] et al. (2019) introduce multi-task learning for instrument recognition. In their work, they propose a method to recognize both pitches and instruments [16]. To augment the data, they employed a Wave Generative Adversarial Network (WaveGAN) architecture to generate audio files [7; 8; 9]. These approaches demonstrate the utilization of various techniques, including feature extraction, deep learning, image processing, and data augmentation, to improve instrument recognition accuracy and handle challenges such as low-quality recordings and polyphonic music.  
Absolutely, transfer learning-based methods are widely used in instrument recognition tasks. Transfer learning leverages pre-trained models on large-scale datasets and adapts them to the specific instrument recognition task. By utilizing the knowledge learned from one domain, these models can generalize well to the instrument recognition domain, even with limited labeled data [17].  
Furthermore, multi-instance multi-label (MIML) methods have also been employed for instrument recognition [6]. MIML considers the fact that music pieces often contain multiple instruments playing simultaneously, making it a multi-label classification problem. In this approach, the music piece is treated as a bag of instances, with each instance representing a segment or frame of the audio. The model learns to classify each segment into different instrument labels, allowing for the recognition of multiple instruments within a single music piece.