LSTAMP

A neural network designed to model a guitar tube amplifier inspired by a project by sdatkinson.

Since Tube Amplifiers typically act non-linearly to the input signal, it becomes an interesting problem to model this using a neural network. LSTM networks are notoriously suited for this kind of task. Adding some 1DConv layers reduces the amount of samples fed into the LSTM and therefore drastically reduces the amount of computational time, allowing for real-time(!) emulation (this was heavily inspired by the work of TSchmitzULG)

The output signal of the amplifier depends on a 'history' h of input samples:

y_t = a₁ x_t + a₂ x_t-1 + ... + a_h x_t-h

Hence we need to create a data set that inputs h samples for every 1 output sample.

The code is written to be executed in an IPython console.

Real time emulation is possible and code for real-time emulation is coming soon.

Training

Various parameters can be set in the code before execution. The standard parameters for the included samples are:

history = 200
batch_size = 4096
hidden_units = 36
conv = [35,12]
learning_rate = 0.01
test_size = 0.2

Once the code is executed LSTAMP can be instantiated using:

LSTAMP = LSTAMP()

when including parameter load_model, one can load a pretrained model.

Once an LSTAMP has been made it can be trained for n epochs using

LSTAMP.fit(n)

Once trained, the model can be saved using

LSTAMP.save(filename)

Amplifying using a trained model

Once a model has been trained and saved it can be used to amplify audio samples.

LSTAMP.amp(filename)

to amplify filename.wav.

Results

Below are the results of a network with above-mentioned hyperparameters for a sample of a Fender Stratocaster into a Silverface amplifier with speaker simulation from Apple's Logic.

As can be seen in the above plot, the model is able to come close already after very few epochs. Although there's visibly more detail in the target file (more harmonics), this difference seems to be inaudible when playing back these results.