## 5 Results  
The training procedures were performed for each architecture and each modeling task: _preamp_ corresponds to the vacuum-tube preamplifier, _limiter_ to the transistor-based limiter amplifier, _horn tremolo_ and _horn chorale_ to the _Leslie speaker_ rotating horn at fast and slow speeds, respectively, and _woofer tremolo_ and _woofer chorale_ to the rotating woofer at the corresponding speeds. Then, the models were tested with samples from the test subset and the audio results are available online ([[https://mchijmma.github.io/DL-AFx/](https://mchijmma.github.io/DL-AFx/)]([https://mchijmma.github.io/DL-AFx/](https://mchijmma.github.io/DL-AFx/))). Figure 5 shows the _mae_, _mfc_cosine_ and _ms_mse_ for all the test subsets. It can be seen that the _mae_ models' performance is similar within each modeling tasks with _limiter_ having the lowest error. Also, _CAFx_ presents the largest errors, with the _Leslie speaker chorale_ settings being the highest. In terms of perceptually-based metrics such as the _mfc_cosine_ and _ms_mse_, the _CRAFx_ and _CWAFx_ models achieved the best scores. This with the exception of the _woofer chorale_ task, where the _CWAFx_ model did not manage to accomplish the task. Overall, _CRAFx_ and _CAFx_ correspond to the highest and lowest scoring models, respectively. Figure 5: _mae_, _mfc_cosine_ and _ms_mse_ values with the test dataset for all the modeling tasks. The results of the listening test for all modeling tasks can be seen in Figure 6 as notched box plots. The end of the notches represents a 95% confidence interval and the end of the boxes represent the first and third quartiles. Also, the green lines illustrate the median rating and the purple circles represent outliers. In general, both anchors and hidden references have the lowest and highest median, respectively. The perceptual findings match closely the objective metrics from Figure 5, since the architectures that explicitly learn long-temporal dependencies, such as _C[pdencies, such as_For selected test samples of the _preamp_ and _limiter_ tasks and for all the different models, Figure 7 shows the input, reference, and output waveforms together with their respective spectrogram. Both in the time-domain and in the frequency-domain, it is observable that the waveforms and spectrograms are in line with the objective and subjective findings. To more closely display the performance of these nonlinear tasks, Figure 8 shows a segment of the respective waveforms. It can be seen how the different models match the waveshaping from the overdriven _preamp_ as well as the attack waveshaping of the _limiter_ when processing the onset of the test sample. Regarding the _Leslie speaker_ modeling task, Figures 9-12 show the different waveforms together with their respective modulation spectrum and spectrogram: Figure 9_horn-tremola_, Figure 10_woofer-tremola_, Figure 11_horn-chorale_ and Figure 12_woofer-chorale_. From the spectra, it is noticeable that _CRAFx_ and _CWAFx_ introduce and match the amplitude and frequency modulations of the reference, whereas _CAFX_ and _WaveNet_ fail to accomplish the time-varying tasks. Figure 8: For the test samples from Figure 7, a segment of the respective waveforms: (**a**) preamp task and (**b**) limiter task when processing the onset of the input audio. Vertical axes represent amplitude. Figure 9: Results with selected samples from the test dataset for the [PERSON] speaker horn-tremolo tasks. (**a**) Waveform, (**b**) modulation spectrum and (**c**) spectrogram. Vertical axes represent amplitude,  
Figure 10: Results with selected samples from the test dataset for the [PERSON] speaker woofer-tremolo tasks. (**a**) Waveform, (**b**) modulation spectrum and (**c**) spectogram. Vertical axes represent amplitude, Gammatone frequency (Hz) and FFT frequency (Hz), respectively. Figure 11: Results with selected samples from the test dataset for the Leslie speaker horn-chorale tasks. (**a**) Waveform, (**b**) modulation spectrum and (**c**) spectrogram.