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# librosa.lpc

librosa.lpc(y, *, order, axis=-1)[source]

Linear Prediction Coefficients via Burg’s method

This function applies Burg’s method to estimate coefficients of a linear filter on `y` of order `order`. Burg’s method is an extension to the Yule-Walker approach, which are both sometimes referred to as LPC parameter estimation by autocorrelation.

It follows the description and implementation approach described in the introduction by Marple. [1] N.B. This paper describes a different method, which is not implemented here, but has been chosen for its clear explanation of Burg’s technique in its introduction.

Parameters:
ynp.ndarray [shape=(…, n)]

Time series to fit. Multi-channel is supported..

orderint > 0

Order of the linear filter

axisint

Axis along which to compute the coefficients

Returns:
anp.ndarray [shape=(…, order + 1)]

LP prediction error coefficients, i.e. filter denominator polynomial. Note that the length along the specified `axis` will be `order+1`.

Raises:
ParameterError
FloatingPointError
• If `y` is ill-conditioned

Examples

Compute LP coefficients of y at order 16 on entire series

```>>> y, sr = librosa.load(librosa.ex('libri1'))
>>> librosa.lpc(y, order=16)
```

Compute LP coefficients, and plot LP estimate of original series

```>>> import matplotlib.pyplot as plt
>>> import scipy
>>> y, sr = librosa.load(librosa.ex('libri1'), duration=0.020)
>>> a = librosa.lpc(y, order=2)
>>> b = np.hstack([[0], -1 * a[1:]])
>>> y_hat = scipy.signal.lfilter(b, [1], y)
>>> fig, ax = plt.subplots()
>>> ax.plot(y)
>>> ax.plot(y_hat, linestyle='--')
>>> ax.legend(['y', 'y_hat'])
>>> ax.set_title('LP Model Forward Prediction')
```