Vocal Acoustic Engine

extracts ~70 acoustic features using audio analysis library and speech analysis library (Praat).

Constructor ```python

engine = VocalAcousticEngine(sample_rate=16000, frame_length_ms=25, hop_length_ms=10, gender=None, # ‘male’ (85–180 Hz), ‘female’ (165–255 Hz), or None (auto 75–300 Hz))

## Primary method ```python
features: VocalFeatures = engine.extract_features(audio: np.ndarray, sr: Optional[int])

VocalFeatures dataclass

F0 (fundamental frequency)

Field	Unit
`f0_mean`	Hz
`f0_std`	Hz
`f0_min`, `f0_max`, `f0_range`	Hz
`f0_confidence`	0–1

Voice quality

Field	Unit	Normal range
`jitter`	%	< 1.04 %
`shimmer`	%	< 3.81 %
`hnr`	dB	> 20 dB
`voice_quality_confidence`	0–1

Prosody

Field	Unit
`speaking_rate`	syllables / sec
`articulation_rate`	syllables / sec (excluding pauses)
`pause_count`	int
`pause_duration_mean`, `pause_duration_total`	seconds
`pitch_variability`	coefficient of variation
`intensity_mean`, `intensity_std`	dB
`prosody_confidence`	0–1

Spectral

Field	Unit
`mfcc`	13-element list
`spectral_centroid`	Hz
`spectral_bandwidth`	Hz
`spectral_rolloff`	Hz
`spectral_flatness`	0–1
`spectral_confidence`	0–1

Clinical markers

Field	Range
`vocal_depression_index`	0–1
`vocal_anxiety_index`	0–1
`vocal_distress_index`	0–1
`prosodic_flatness`	0–1 (anhedonia marker)
`clinical_confidence`	0–1

Quality

Field	Notes
`overall_quality`	0–100
`voiced_fraction`	0–1
`snr`	dB
`audio_duration`	seconds

Library dependencies

audio analysis library
speech analysis library (Praat)
SciPy (scipy.signal, scipy.stats)
NumPy

​Constructor ```python

​VocalFeatures dataclass

​F0 (fundamental frequency)

​Voice quality

​Prosody

​Spectral

​Clinical markers

​Quality

​Library dependencies