grafx.processors.stereo
- class StereoGain
Bases:
ModuleA simple stereo-to-stereo or mono-to-stereo gain.
We use simple channel-wise constant multiplication with a gain vector. The gain is assumed to be in log scale, so we apply exponentiation before multiplying it to the stereo signal.
\[ y[n] = \exp (g_{\mathrm{log}}) \cdot u[n]. \]Hence, the learnable parameter is \(p = \{ g_{\mathrm{log}} \}.\)
- forward(input_signals, log_gain)
Processes input audio with the processor and given parameters.
- Parameters:
input_signals (
FloatTensor, \(B \times C \times L\)) – A batch of input audio signals, either mono or stereo.log_magnitude (
FloatTensor, \(B \times 2\)) – A log-magnitude vector of the FIR filter.
- Returns:
A batch of output signals of shape \(B \times 2 \times L\).
- Return type:
FloatTensor
- parameter_size()
- Returns:
A dictionary that contains each parameter tensor’s shape.
- Return type:
Dict[str, Tuple[int, ...]]
- class SideGainImager
Bases:
ModuleStereo imager with side-channel loudness control.
We multiply the input’s side signal \(u_{\mathrm{s}}[n]\), i.e., left \(u_{\mathrm{l}}[n]\) minus right \(u_{\mathrm{r}}[n]\), with a gain parameter \(g \in \mathbb{R}\) to control the stereo width. The mid and side outputs are given as
\[\begin{split} y_{\mathrm{m}}[n] &= u_{\mathrm{l}}[n] + u_{\mathrm{r}}[n], \\ y_{\mathrm{s}}[n] &= \exp (g) \cdot (u_{\mathrm{l}}[n] - u_{\mathrm{r}}[n]). \end{split}\]Hence, the learnable parameter is \(p = \{ g_{\mathrm{log}} \}.\)
- forward(input_signals, log_gain)
Processes input audio with the processor and given parameters.
- Parameters:
input_signals (
FloatTensor, \(B \times C \times L\)) – A batch of input audio signals; must be stereo.log_magnitude (
FloatTensor, \(B \times P \:\!\)) – A log-magnitude vector of the FIR filter.
- Returns:
A batch of output signals of shape \(B \times 2 \times L\).
- Return type:
FloatTensor
- parameter_size()
- Returns:
A dictionary that contains each parameter tensor’s shape.
- Return type:
Dict[str, Tuple[int, ...]]
- class MonoToStereo
Bases:
ModuleA simple mono-to-stereo conversion.
- forward(input_signals)
Processes input audio with the processor and given parameters.
- Parameters:
input_signals (
FloatTensor, \(B \times 1 \times L\)) – A batch of input audio signals; must be mono.- Returns:
A batch of output signals of shape \(B \times 2 \times L\).
- Return type:
FloatTensor
- parameter_size()
- Returns:
A dictionary that contains each parameter tensor’s shape.
- Return type:
Dict[str, Tuple[int, ...]]
- class StereoToMidSide(normalize=True)
Bases:
ModuleA simple stereo-to-mid-side conversion.
- forward(input_signals)
Processes input audio with the processor and given parameters.
- Parameters:
input_signals (
FloatTensor, \(B \times 2 \times L\)) – A batch of input audio signals; must be stereo.- Returns:
A batch of output signals of shape \(B \times 2 \times L\).
- Return type:
FloatTensor
- parameter_size()
- Returns:
A dictionary that contains each parameter tensor’s shape.
- Return type:
Dict[str, Tuple[int, ...]]
- class MidSideToStereo(normalize=True)
Bases:
ModuleA simple mid-side-to-stereo conversion.
- forward(mid, side)
Processes input audio with the processor and given parameters.
- Parameters:
mid (
FloatTensor, \(B \times 1 \times L\)) – A batch of mid audio signals.side (
FloatTensor, \(B \times 1 \times L\)) – A batch of side audio signals.
- Returns:
A batch of output signals of shape \(B \times 2 \times L\).
- Return type:
FloatTensor
- parameter_size()
- Returns:
A dictionary that contains each parameter tensor’s shape.
- Return type:
Dict[str, Tuple[int, ...]]