# A Review of Neural Network-Based Emulation of Guitar Amplifiers
## 3 Neural Network-Based Methods
### Data Sets  
The performance of any of the networks, no matter what category of approach they may fall under, is directly determined by a number of choices made regarding the training process. Notably, the choice of data set is critical. The performance of neural networks for any given task depends heavily on the data set they are given during the training phase. Indeed, the network needs to be exposed to a wide range input-output pairings in order to learn an accurate mapping for the majority of cases it will encounter during its use. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Pros** & \multicolumn{2}{c}{**Cons**} \\ \hline
**Black-Box** & - & Proven Real-Time Capabilities e.g., [23] & - & Non interpretable Difficulty of parametric models \\ \hline
**Gray-Box** & - & More interpretable & - & Possible unstable training [41] \\  & - & - & No Proven Real-Time Capabilities \\ \hline
**White-Box** & - & More interpretable Parametric Improved accuracy over traditional white-box & - & Same difficulties with Real-Time as traditional white-box [46] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the modelling approaches that utilize deep learning as well as their advantages and downsides. There exists a number of data sets used for the task of amplifier modelling, all bearing certain similarities. Almost all of the data used throughout the state-of-the-art comprises clean guitar Direct Input (DI) sent through either the analog device or a SPICE simulation of the device. However, the data used depends on the approach. In black-box approaches, only input-output guitar recordings are necessary for the neural network training, whereas for gray- or white-box different or additional data is required. In the gray-box approaches presented in [41; 42; 43; 4], component values of the internal circuit are also used in the training data. In the white-box approach of [PERSON] et al. [46], only the circuit component values are used. In the black- and gray-box approaches, certain aspects of the training data have a significant impact on the resulting model. These aspects are, namely, the sampling rate, the length, and type of the data. The sampling rate dictates the audio quality of the simulation and impacts its real-time processing capabilities. The data used to train the WaveNet from [25] was obtained from a SPICE simulation of a Fender Bassman 56F-A preamplifier applied to DI from a Freesound data set for audio tagging [47]. A total of 4 h of data was used for the train set and 20 min for validation split into 100 ms segments and a random gain value in the range of [\(-15\) dB, \(15\) dB] was applied to the inputs for more dynamic range. All data were recorded with a sampling rate of 44.1 kHz. In their follow-up article [26], [PERSON] et al. show that as little as three minutes of data is sufficient for the training of the convolutional networks, although final results presented were obtained with five minutes of data (50% guitar and 50% bass) from the IDMT-SMT-Guitar/Bass data sets from [48; 49]. The sampling rate for all recordings was 44.1 kHz. These data sets contain a variety of single note recordings of various different playing styles with varying pickups. The raw inputs from this data set were sent through three effects pedals: Ibanez Tube Streamer, Boss DS-1, and an Electro-Harmonix Big Muff Pi. This data set was also used to train the recurrent networks from [33]. The amplifier models of [23] used a different training set than the pedal emulation, taken from a pre-existing data set. This data set was tailor-made for this modelling task and was published in [50]. It includes five different styles of guitar sounds sent through various guitar amplifiers with their gain parameters set to ten different levels. The audio used in [23] consists of around three minutes of guitar audio recorded at 44.1 kHz with the training set consisting of 2 min 43 s of audio. This data was used to train both the WaveNet style model and the LSTM.