# Detecting Selected Instruments in the Sound Signal
## 2 Related Work  
The popularity and spread of machine learning, artificial intelligence, and similar solutions also have an impact on the field of acoustic signal analysis and processing [12; 13; 14; 15; 16]. Machine learning methods, in particular, artificial neural network (_ANN_), are an alternative to classic methods such as the k-nearest neighbor or the random trees algorithms. Their effectiveness mainly contributed to improving the quality of speech recognition applications and automatic music recommendation systems [17]. Compared to other deep learning techniques, especially convolutional neural networks (_CNN_) are considered extremely effective in the context of audio processing [18; 19; 20; 21]. Apart from those mentioned, other applications of the _CNN_ network include transcription, voice detection, chord recognition, beat detection, defining time signatures, frequency response analysis, and articulation recognition.  
Convolutional networks [22; 23; 24] are also widely used in automatic instrument recognition systems. In [22], research was carried out consisting of carrying out the constant Q transform (_CQT_) of the signal, thus obtaining the input matrix for the convolutional network, which consisted of an input layer, two convolutional layers, and two dense layers. Moreover, the Rectified Linear Unit (_ReLU_) [25] was used as an activation function. The ReLU function works in the way that if the input x is less than 0, the output is equal to 0; if the input x is greater than 0, the output is equal to the input. The training set (Medley-solos-db, which is described in Section 3.1) consisted of 158 min of recordings of eight different instruments: clarinet, electric guitar, flute, piano, tenor saxophone, trumpet, violin, and a female voice, which in this context can be treated as a stringed instrument. The test set contained tracks with a total length of 208 min. These sets were unbalanced, so the recordings of some of the instruments constituted only a small percentage of the total set. Ten different configurations were tested, which signaled the general trend that increasing the number of network parameters led to better network performance. The maximum efficiency of the algorithm that was achieved was 74% correct decisions.  
In [26], the authors developed a deep learning architecture that is relatively small (in terms of a number of parameters). The main idea was to create an autoencoder to discover a set of embedded representations of the instrument's sound and then provide them to the specialized prototype layer. The mean accuracy obtained for the model based on the Medley-solos-db dataset is 67.3%.  
[PERSON] et al. presented a relatively simple architecture that consists of a few steps [27]. Firstly, they generated Mel-spectrograms and a set of _MFCC_ values, and then (due to a noticeable class imbalance in the Medley-solos-db dataset), the SMOTE algorithm was performed. The outcome of SMOTE was transmitted to the convolutional network for classification. The authors obtained stunning accuracy of over 99%. However, they provided the numerical results only for the training and validation subsets, so the real test accuracy is still unknown.  
The actual state of the art is [28]. The authors developed a very sophisticated multi-level deep architecture. They are using it for creating a kind of foundation model for different music-oriented tasks, which is further prepared inter alia for instrument recognition tasks. The final accuracy for Medley-solos-db is 76.1%.  
Other works on instrument detection in sound signals can also be found [29; 30; 31]. However, these are works carried out on different datasets. They differ in the number of different instruments, the number of samples, the degree of balance, the sound of selected instruments, the length of a single recording, and the method of recording (e.g., MIDI--containing individual notes, wav--containing the actual sound). Also, the purpose of these studies is often different.