# A Review of Neural Network-Based Emulation of Guitar Amplifiers
## 3 Neural Network-Based Methods  
The use of neural networks has seen a rise in practically all scientific fields since the start of the 21 st century thanks to increased computational resources and the availability of larger data sets. These ANN have subsequently been able to achieve a level of accuracy that far surpasses that of other methods used previously. This is the case in a number of fields, particularly in computer vision with the use of Convolutional Neural Networks (CNN) and in speech recognition and machine translation with the use of Recurrent Neural Networks (RNN). CNN are networks specialized in processing grids of values such as images through convolution operations that serve to extract salient features in the pixels. RNN are tailored to processing sequences of values and are particularly suited for time-dependent or sequence-based tasks such as language translation or natural language processing [22]. A neural network is a class of machine learning algorithm whose basic structure is made up of inter-linked neurons and often nonlinear activation functions. These neurons are organized into layers and are made up of a set of mathematical operations wherein the input to the layer is multiplied by a set of learnt weights, bias is optionally added, and the activation function is applied to the output which is sent to the next layer. The value of the weights are learnt by the network via an optimization task in which a given distance between the target and the network output is minimized. This enables the network to learn complex nonlinear mappings between the inputs and outputs which would make this method suited to the task of distortion modelling. We can see similarities between this process and certain methods described previously in the black- and gray-box categories of methods. For example, an architecture comprisinga 1D convolutional layer followed by a nonlinear activation can be considered as a Wiener model (linear filter followed by a nonlinearity) [23]. Certain nonlinear activation functions present in neural networks, such as the sigmoid or hyperbolic tangent, appear frequently in the literature of VA modelling [6]. Additionally, certain architectures that exist, designed for times series and sequence modelling, are well suited to the amplifier emulation task and also bear similarities with classic DSP elements. For example, [PERSON] et al. [4] show an equivalence between infinite impulse response (IIR) filters and RNN. Taking into account such similarities between traditional VA modelling and neural networks, it seems natural to extend the use of ANN to this simulation task. However, amplifier modelling via neural networks is not a straightforward task. The deep, convolutional, and data-hungry methods used in imaging for example can pose a problem for real-time use. In addition, processing raw waveforms in the time-domain means that we have to deal with extremely high temporal dimensionality because of the sampling rates needed in order to achieve high-quality audio. Indeed, a minimum sampling rate of 44.1 kHz is required [24]. Moreover, the sampling rate used in this type of application is often a constraint imposed by the hardware. This is a less detrimental factor in speech processing where sampling rates of around 16 kHz, or even 8 kHz, can be sufficient. These factors increase both the computational cost and the complexity of the task and render music deep learning challenging. A number of DL architectures have appeared in the state-of-the-art of distortion circuit modelling techniques in recent years including various configurations of both convolutional and recurrent layers. Here, we study the recent works that have appeared in this state-of-the-art. Each aspect of the neural modelling problem will be discussed with recapitulative tables that aim to summarize and clarify the situation.