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