kopia lustrzana https://github.com/corrscope/corrscope
Merge pull request #408 from corrscope/decouple-edge-and-kernel
nyanpasu64
GitHub
commit
79f443da06
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@ -8,7 +8,7 @@
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### Major Changes
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- Rewrite the trigger algorithm to enhance determinism (#403)
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- Rewrite the trigger algorithm to enhance determinism and reduce errors when DC offset varies within a frame (#403, #408)
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- Triggering still makes mistakes, especially when DC offset varies within a frame (eg. NES 75% pulse changing volumes). This may be addressed in the future.
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- Changed default triggering settings as well.
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@ -16,6 +16,7 @@
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- Fix passing absolute .wav paths into corrscope CLI (#398)
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- Fix preview error when clearing "Trigger/Render Width" table cells (#407)
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- Add control for DC removal rate (#408)
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## 0.7.1
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@ -1095,6 +1095,7 @@ class ChannelModel(qc.QAbstractTableModel):
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Column("render_stereo", str, None, "Render Stereo\nDownmix"),
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Column("trigger_width", int, 1, "Trigger Width ×", always_show=True),
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Column("render_width", int, 1, "Render Width ×", always_show=True),
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Column("trigger__mean_responsiveness", float, None, "DC Removal\nRate"),
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Column("trigger__sign_strength", float, None),
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Column("trigger__buffer_strength", float, None),
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Column("trigger__responsiveness", float, None, "Buffer\nResponsiveness"),
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@ -295,6 +295,17 @@ class MainWindow(QWidget):
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with append_widget(
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s, QGroupBox, title=tr("Input Data Preprocessing"), layout=QFormLayout
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):
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with add_row(
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s,
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tr("DC Removal Rate"),
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BoundDoubleSpinBox,
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name="trigger__mean_responsiveness",
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minimum=0,
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maximum=1,
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singleStep=0.25,
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):
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pass
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with add_row(
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s,
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tr("Sign Triggering\n(for triangle waves)"),
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@ -258,6 +258,7 @@ class PerFrameCache:
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class CorrelationTriggerConfig(
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MainTriggerConfig,
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always_dump="""
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mean_responsiveness
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pitch_tracking
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slope_strength slope_width
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"""
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@ -267,6 +268,7 @@ class CorrelationTriggerConfig(
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# get_trigger()
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# Edge/area finding
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sign_strength: float = 0
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mean_responsiveness: float = 1.0
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edge_strength: float
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# Slope detection
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@ -348,6 +350,7 @@ class CorrelationTrigger(MainTrigger):
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_edge_finder: "npt.NDArray[f32]"
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"""(const) [A+B] Amplitude"""
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_prev_mean: float
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_prev_period: Optional[int]
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_prev_slope_finder: "Optional[npt.NDArray[f32]]"
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"""(mutable) [A+B] Amplitude"""
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@ -369,12 +372,7 @@ class CorrelationTrigger(MainTrigger):
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# Updated with tightly windowed old data at various pitches.
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self._corr_buffer = np.zeros(self.A + self.B, dtype=f32)
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# (const) Added to self._buffer. Nonzero if edge triggering is nonzero.
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# Left half is -edge_strength, right half is +edge_strength.
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# ASCII art: --._|‾'--
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self._edge_finder = self._calc_step()
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assert self._edge_finder.dtype == f32
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self._prev_mean = 0.0
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# Will be overwritten on the first frame.
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self._prev_period = None
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self._prev_slope_finder = None
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@ -393,22 +391,6 @@ class CorrelationTrigger(MainTrigger):
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else:
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self._spectrum_calc = DummySpectrum()
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def _calc_step(self) -> np.ndarray:
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"""Step function used for approximate edge triggering. Has length A+B."""
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# Increasing buffer_falloff (width of buffer)
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# causes buffer to affect triggering, more than the step function.
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# So we multiply edge_strength (step function height) by buffer_falloff.
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cfg = self.cfg
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edge_strength = cfg.edge_strength * cfg.buffer_falloff
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step = np.empty(self.A + self.B, dtype=f32) # type: np.ndarray[f32]
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step[: self.A] = -edge_strength / 2
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step[self.A :] = edge_strength / 2
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step *= windows.gaussian(self.A + self.B, std=self.A / 3)
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return step
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def _calc_slope_finder(self, period: float) -> Optional[np.ndarray]:
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"""Called whenever period changes substantially.
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Returns a kernel to be correlated with input data to find positive slopes,
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@ -440,7 +422,7 @@ class CorrelationTrigger(MainTrigger):
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# Convert sizes to full samples (not trigger subsamples) when indexing into
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# _wave.
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# _trigger_diameter is defined as inclusive. The length of correlate_valid()'s
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# _trigger_diameter is defined as inclusive. The length of find_peak()'s
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# corr variable is (A + _trigger_diameter + B) - (A + B) + 1, or
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# _trigger_diameter + 1. This gives us a possible triggering range of
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# _trigger_diameter inclusive, which is what we want.
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@ -461,12 +443,20 @@ class CorrelationTrigger(MainTrigger):
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signs = sign_times_peak(data)
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data += cfg.sign_strength * signs
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# Remove mean from data
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data -= np.add.reduce(data) / data.size
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# Remove mean from data, if enabled.
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mean = np.add.reduce(data) / data.size
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period_data = data - mean
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if cfg.mean_responsiveness:
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self._prev_mean += cfg.mean_responsiveness * (mean - self._prev_mean)
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if cfg.mean_responsiveness != 1:
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data -= self._prev_mean
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else:
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data = period_data
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# Use period to recompute slope finder (if enabled) and restrict trigger
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# diameter.
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period = get_period(data, self.subsmp_per_s, self.cfg.max_freq, self)
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period = get_period(period_data, self.subsmp_per_s, cfg.max_freq, self)
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cache.period = period * stride
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semitones = self._is_window_invalid(period)
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@ -484,12 +474,30 @@ class CorrelationTrigger(MainTrigger):
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else:
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slope_finder = self._prev_slope_finder
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if cfg.edge_strength:
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# I want a half-open cumsum, where edge_score[0] = 0, [1] = data[A], [2] =
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# data[A] + data[A+1], etc. But cumsum is inclusive, which causes tests to
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# fail. So subtract 1 from the input range.
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edge_score = np.cumsum(data[self.A - 1 : len(data) - self.B])
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# The optimal edge alignment is the *minimum* cumulative sum, so invert
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# the cumsum so the minimum amplitude maps to the highest score.
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edge_score *= -cfg.edge_strength
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else:
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edge_score = None
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# array[A+B] Amplitude
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corr_kernel: np.ndarray = self._corr_buffer * self.cfg.buffer_strength
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corr_kernel += self._edge_finder
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corr_kernel: np.ndarray = self._corr_buffer * cfg.buffer_strength
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if slope_finder is not None:
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corr_kernel += slope_finder
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# `corr[x]` = correlation of kernel placed at position `x` in data.
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# `corr_kernel` is not allowed to move past the boundaries of `data`.
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corr = signal.correlate_valid(data, corr_kernel)
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if edge_score is not None:
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corr += edge_score
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# Don't pick peaks more than `period * trigger_radius_periods` away from the
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# center.
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if cfg.trigger_radius_periods and period != UNKNOWN_PERIOD:
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@ -497,17 +505,11 @@ class CorrelationTrigger(MainTrigger):
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else:
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trigger_radius = None
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def correlate_valid(
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data: np.ndarray, corr_kernel: np.ndarray, radius: Optional[int]
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) -> int:
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"""Returns kernel offset (≥ 0) relative to data, which maximizes correlation.
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kernel is not allowed to move past the boundaries of data.
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If radius is set, the returned offset is limited to ±radius from the center of
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correlation.
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def find_peak(corr: np.ndarray, radius: Optional[int]) -> int:
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"""If radius is set, the returned offset is limited to ±radius from the
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center of correlation.
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"""
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# returns double, not single/f32
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corr = signal.correlate_valid(data, corr_kernel)
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begin_offset = 0
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if radius is not None:
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@ -535,7 +537,7 @@ class CorrelationTrigger(MainTrigger):
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return peak_offset
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# Find correlation peak.
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peak_offset = correlate_valid(data, corr_kernel, trigger_radius)
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peak_offset = find_peak(corr, trigger_radius)
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trigger = trigger_begin + stride * (peak_offset)
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del data
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