lpc#

diffsptk.LPC#: alias of LinearPredictiveCodingAnalysis

class diffsptk.LinearPredictiveCodingAnalysis(frame_length: int, lpc_order: int, eps: float | None = None, device: device | None = None, dtype: dtype | None = None)[source]#

See this page for details. Double precision is recommended.

Parameters:

frame_lengthint > M: The frame length, \(L\).
lpc_orderint >= 0: The order of the LPC coefficients, \(M\).
epsfloat >= 0 or None: A small value to improve numerical stability. If None, automatically set based on the data type.
devicetorch.device or None: The device of this module.
dtypetorch.dtype or None: The data type of this module.

forward(x: Tensor) → Tensor[source]#

Perform LPC analysis.

Parameters:

xTensor [shape=(…, L)]: The framed waveform.

Returns:

outTensor [shape=(…, M+1)]: The gain and LPC coefficients.

Examples

>>> import diffsptk
>>> lpc = diffsptk.LPC(5, 2)
>>> x = diffsptk.ramp(1, 5) * 0.1
>>> a = lpc(x)
>>> a
tensor([ 0.5054, -0.8140,  0.1193])

diffsptk.functional.lpc(x: Tensor, lpc_order: int, eps: float | None = None) → Tensor[source]#

Perform LPC analysis.

Parameters:

xTensor [shape=(…, L)]: The famed waveform.
lpc_orderint >= 0: The order of the LPC coefficients, \(M\).
epsfloat >= 0 or None: A small value to improve numerical stability. If None, automatically set based on the input data type.

Returns:

outTensor [shape=(…, M+1)]: The gain and LPC coefficients.