F0DerivativeExtractor#

Defined in: voxatlas.features.acoustic.pitch.derivative

class voxatlas.features.acoustic.pitch.derivative.F0DerivativeExtractor[source]#

Bases: BaseExtractor

Extract the acoustic.pitch.f0.derivative feature within the VoxAtlas pipeline.

This public extractor defines the reusable API for computing acoustic.pitch.f0.derivative from VoxAtlas structured inputs. It consumes None units and produces values aligned to frame units, making the extractor a stable pipeline node that can be cited independently of the surrounding execution machinery.

Algorithm#

The extractor computes a first-order temporal difference of the upstream contour while preserving the original frame alignment.

  1. Dependency retrieval The base frame-level contour is read from the feature store. This may be an \(f_0\) contour or an amplitude-like envelope, depending on the feature family.

  2. Finite difference The code applies the backward difference

    \[d_t = x_t - x_{t-1},\]

    with missing values preserved when the upstream contour is undefined.

  3. Output alignment The derivative is emitted at the same frame times as the source contour so later extractors can compare slope-like dynamics and local changes without reindexing.

Notes

This extractor declares the upstream dependencies [‘acoustic.pitch.f0’] and is executed only after those features are available in the pipeline feature store.

Examples

>>> import numpy as np
>>> from voxatlas.features.acoustic.pitch.derivative import F0DerivativeExtractor
>>> from voxatlas.features.feature_input import FeatureInput
>>> from voxatlas.features.feature_output import VectorFeatureOutput
>>> from voxatlas.pipeline.feature_store import FeatureStore
>>> store = FeatureStore()
>>> base = VectorFeatureOutput(
...     feature="acoustic.pitch.f0",
...     unit="frame",
...     time=np.array([0.0, 0.01, 0.02], dtype=np.float32),
...     values=np.array([100.0, 110.0, np.nan], dtype=np.float32),
... )
>>> store.add("acoustic.pitch.f0", base)
>>> feature_input = FeatureInput(audio=None, units=None, context={"feature_store": store})
>>> out = F0DerivativeExtractor().compute(feature_input, {})
>>> out.values.tolist()
[nan, 10.0, nan]
name: str = 'acoustic.pitch.f0.derivative'#
input_units: str | None = None#
output_units: str | None = 'frame'#
dependencies: list[str] = ['acoustic.pitch.f0']#
default_config: dict = {}#
compute(feature_input, params)[source]#

Compute the extractor output for a single pipeline invocation.

This method is the reusable execution entry point for the extractor. It receives the standard FeatureInput bundle, applies the configured algorithm, and returns feature values aligned to the extractor output units for storage in the pipeline feature store.

Parameters:

  • feature_input (object) – Structured extractor input bundling audio, hierarchical units, and execution context for this feature computation.

  • params (object) – Resolved feature configuration for this invocation. Keys are feature-specific and merged from defaults and pipeline settings.

Returns:

Structured output aligned to the frame unit level when applicable.

Return type:

FeatureOutput

Examples

>>> import numpy as np
>>> from voxatlas.features.acoustic.pitch.derivative import F0DerivativeExtractor
>>> from voxatlas.features.feature_input import FeatureInput
>>> from voxatlas.features.feature_output import VectorFeatureOutput
>>> from voxatlas.pipeline.feature_store import FeatureStore
>>> store = FeatureStore()
>>> base = VectorFeatureOutput(
...     feature="acoustic.pitch.f0",
...     unit="frame",
...     time=np.array([0.0, 0.01, 0.02], dtype=np.float32),
...     values=np.array([100.0, 110.0, np.nan], dtype=np.float32),
... )
>>> store.add("acoustic.pitch.f0", base)
>>> feature_input = FeatureInput(audio=None, units=None, context={"feature_store": store})
>>> result = F0DerivativeExtractor().compute(feature_input, {})
>>> result.unit
'frame'