pyroomacoustics.experimental.deconvolution module

pyroomacoustics.experimental.deconvolution.deconvolve(y, s, length=None, thresh=0.0)

Deconvolve an excitation signal from an impulse response

Parameters:

• y (ndarray) – The recording
• s (ndarray) – The excitation signal
• length (int, optional) – the length of the impulse response to deconvolve
• thresh (float, optional) – ignore frequency bins with power lower than this

pyroomacoustics.experimental.deconvolution.wiener_deconvolve(y, x, length=None, noise_variance=1.0, let_n_points=15, let_div_base=2)

Deconvolve an excitation signal from an impulse response

We use Wiener filter

Parameters:

• y (ndarray) – The recording
• x (ndarray) – The excitation signal
• length (int, optional) – the length of the impulse response to deconvolve
• noise_variance (float, optional) – estimate of the noise variance
• let_n_points (int) – number of points to use in the LET approximation
• let_div_base (float) – the divider used for the LET grid