kopia lustrzana https://github.com/erdewit/HiFiScan
Correction
rodzic
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@ -284,13 +284,13 @@ The default is to create a phase-neutral (aka linear-phase)
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impulse response. It's also possible to create a minimum-phase response.
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Even everything in between is possible. This is done with the **Causality**
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parameter, where 0% is phase-neutral and 100% is minimum-phase.
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Varying the causality smoothly transitions between the extremes:
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By varying the causality a smooth transition between the extremes is made:
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.. image:: images/causality-waveform.png
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The latency decreases proportional to the causality.
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At 100% the response becomes zero-latency and purely causal, where only the current
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and later sound samples are used. The pre-echo is eliminated at the price of
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and past sound samples are used. The pre-echo is eliminated at the price of
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twice the post-echo.
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Note that despite the name of "minimum-phase"
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this setting actually incurs the most phase distortion, which can get severe with
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@ -153,7 +153,8 @@ class Analyzer:
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"""Interpolated target curve."""
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return self.interpolateCorrection(self._target)
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def interpolateCorrection(self, corr: Correction) -> Optional[np.ndarray]:
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def interpolateCorrection(
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self, corr: Optional[Correction]) -> Optional[np.ndarray]:
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"""
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Logarithmically interpolate the correction to a full-sized array.
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"""
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@ -244,7 +245,7 @@ class Analyzer:
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dbRange: Maximum attenuation in dB (power).
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kaiserBeta: Shape parameter of the Kaiser tapering window.
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smoothing: Strength of frequency-dependent smoothing.
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causality: 0 = linear-phase a-causal, 1 = minimal-phase causal.
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causality: 0 = linear-phase a-causal, 1 = minimum-phase causal.
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"""
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freq, H2 = self.H2(smoothing)
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# Apply target curve.
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@ -411,7 +412,7 @@ def transform_causality(x: np.ndarray, causality: float = 1) -> np.ndarray:
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the given impulse.
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Params:
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causality: 0 = linear-phase, 1 = minimal-phase and
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causality: 0 = linear-phase, 1 = minimum-phase and
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in-between values smoothly transition between these two.
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https://www.rle.mit.edu/dspg/documents/AVOHomoorphic75.pdf
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