FormantTracksExtractor#

Defined in: voxatlas.features.phonology.formant.tracks

class voxatlas.features.phonology.formant.tracks.FormantTracksExtractor[source]#

Bases: BaseExtractor

Extract the phonology.formant.tracks feature within the VoxAtlas pipeline.

This public extractor defines the reusable API for computing phonology.formant.tracks from VoxAtlas structured inputs. It consumes phoneme 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 derives vowel formant structure from aligned phoneme segments and then aggregates those measurements to the declared unit level.

  1. Segment selection Vowel-bearing phoneme spans are isolated from the aligned unit table, and the corresponding waveform segments are converted into short analysis frames.

  2. Resonance estimation Linear-predictive analysis or Parselmouth formant tracking is used to estimate \(F_1\), \(F_2\), and \(F_3\) for each analysis frame.

  3. Metric computation Linear-predictive analysis or Parselmouth formant tracking is used to recover resonant frequencies \(F_1\), \(F_2\), and \(F_3\) over vowel-bearing segments.

  4. Packaging The resulting statistic is aligned to frame units for use in subsequent phonological or conversation-level analyses.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from voxatlas.audio.audio import Audio
>>> from voxatlas.features.feature_input import FeatureInput
>>> from voxatlas.features.phonology.formant.tracks import FormantTracksExtractor
>>> from voxatlas.units import Units
>>> audio = Audio(waveform=np.zeros(800, dtype=np.float32), sample_rate=8000)
>>> phonemes = pd.DataFrame(columns=["id", "start", "end", "label"])
>>> units = Units(phonemes=phonemes)
>>> params = FormantTracksExtractor.default_config.copy()
>>> params["use_parselmouth"] = False
>>> out = FormantTracksExtractor().compute(FeatureInput(audio=audio, units=units, context={}), params)
>>> ("F1" in out.values.columns, out.unit)
(True, 'frame')
name: str = 'phonology.formant.tracks'#
input_units: str | None = 'phoneme'#
output_units: str | None = 'frame'#
dependencies: list[str] = []#
default_config: dict = {'frame_length': 0.025, 'frame_step': 0.01, 'language': None, 'lpc_order': 10, 'max_formant': 5500.0, 'resource_root': None, 'use_parselmouth': True}#
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
>>> import pandas as pd
>>> from voxatlas.audio.audio import Audio
>>> from voxatlas.features.feature_input import FeatureInput
>>> from voxatlas.features.phonology.formant.tracks import FormantTracksExtractor
>>> from voxatlas.units import Units
>>> audio = Audio(waveform=np.zeros(800, dtype=np.float32), sample_rate=8000)
>>> phonemes = pd.DataFrame(columns=["id", "start", "end", "label"])
>>> units = Units(phonemes=phonemes)
>>> params = FormantTracksExtractor.default_config.copy()
>>> params["use_parselmouth"] = False
>>> result = FormantTracksExtractor().compute(FeatureInput(audio=audio, units=units, context={}), params)
>>> result.values.empty
True