# A Review of Neural Network-Based Emulation of Guitar Amplifiers
## 3 Neural Network-Based Methods
### Real-Time Capabilities  
The audio quality of the prediction is not the only aspect that requires evaluation. The real-time capabilities are a crucial aspect to take into account when studying any VA model and can also be presented as an objective metric of the quality of the emulation. This is illustrated in the last two methods cited in Table 6 that take into account the computation times of the methods. A major factor to take into account when modelling analog audio devices is the real-time constraint. If the solution implemented cannot be used in real-time then it is of little use to the end-users. The real-time constraint for this type of application is approximately 10 ms [3]. Any latency above this value is likely to be perceived by the musician and hinder their playing. The latency produced in digital audio processing is known as round-trip latency that is not limited to only the computation time (CT) of the DSP algorithm used [3]. Analog-to-Digital and then Digital-to-Analog conversion can add up to a 1 ms to the round-trip latency. Additionally, there is a latency inherent to the processing in buffers used in audio interfaces and Digital Audio Workstations (DAW). In order to process a buffer of audio, a delay of that buffer size is added to the round-trip latency which can quickly exceed the real-time constraint. For example, when processing buffers of 256 samples at 44.1 kHz, the time necessary to fill this buffer is around 5.8 ms, which significantly decreases the time left for \begin{table}
\begin{tabular}{c c} \hline \hline
**Objective** & **Subjective** \\ \hline MSE [31], RMSE [30; 35], NRMSE [35], Spectrogram [35; 39], Power Spectrum [35], Harmonic Analysis using ESS [35], Waveform plot [23; 33; 35; 39], SNR [35], Difference in harmonic content [35], MAE [37; 38], FFT magnitude [39], Input-output waveshaping curves [39], ESR [23; 25; 26; 33], MSD [37], Frequency spectrum [25; 26], MS\_MSE [38], MFCC\_COSINE [38], Spectra of 1245-Hz sinusoid to study aliasing [26], AME [42], CT [23; 25; 26; 35], Custom metric that takes into account RMSE + CT [35] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the evaluation methods for network validation after training. the DSP computations. This means that using DL architectures with a lot of computations and layers is complicated as it either entails too high a latency or excessive CPU usage which prohibits its use. A number of the architectures present in the state-of-the-art are capable of being used in real-time to varying degrees but caveats also exist. The architectures presented here although often capable of real-time processing, some of which are light-weight enough to work in real-time on CPU, are still computationally heavy and have not been demonstrated to be able to achieve real-time speeds for sampling rates over 44.1 kHz. Indeed, the hybrid convolutional and recurrent architecture from [35] utilizes parallelization on GPU in order to process the data in real-time which poses a problem for use in a practical setting in which CPU processing is required. The autoencoder architectures from [38] have been demonstrated using a sampling rate of 16 kHz, which is insufficient for high-quality audio applications. The shallow TCN from [28] are capable of real-time use only for input buffer sizes over 1024 samples, which, at 44.1 kHz, incurs a latency of approximately 23.2 ms, which is double the latency required for real-time use of around 10 ms. Finally, the architectures presented in [23], although capable of real-time processing, remain computationally heavy, even for a sampling rate of 44.1 kHz, which is the lower bound for high quality audio in music applications. Moreover, digital implementations of analog audio effects usually introduce aliasing into the signal, and to remedy this, anti-aliasing techniques are used which often require upsampling the signals by a factor of eight [6].