OneTrack

OneTrack(
    maximum_formant,
    sound=None,
    samples=None,
    sampling_frequency=None,
    xmin=0.0,
    n_formants=4,
    window_length=0.025,
    time_step=0.002,
    pre_emphasis_from=50,
    smoother=Smoother(method='dct_smooth_regression'),
    loss_fun=Loss(method='lmse'),
    agg_fun=Agg(method='agg_sum'),
    heuristics=[],
)

A single formant track.

You can provide either

• A parselmouth Sound object to the sound argument

xor

• An array of audio samples to the samples argument
• The sampling frequency to the sampling_frequency argument
• Any optional time offset to the xmin argument.

If a Sound object is passed to sound, any values passed to samples, sampling_frequency and xmin are ignored.

Parameters

Name	Type	Description	Default
sound	pm.Sound	A parselmouth.Sound object.	None
samples	np.ndarray	A numpy array of audio samples.	None
sampling_frequency	float	The audio sampling frequency.	None
xmin	float	The time offset for the audio. Defaults to 0.0.	0.0
maximum_formant	float	max formant	required
n_formants	int	The number of formants to track. Defaults to 4.	4
window_length	float	Window length of the formant analysis. Defaults to 0.025.	0.025
time_step	float	Time step of the formant analyusis window. Defaults to 0.002.	0.002
pre_emphasis_from	float	Pre-emphasis threshold. Defaults to 50.	50
smoother	Smoother	The smoother method to use. Defaults to Smoother().	Smoother(method='dct_smooth_regression')
loss_fun	Loss	The loss function to use. Defaults to Loss().	Loss(method='lmse')
agg_fun	Agg	The loss aggregation function to use. Defaults to Agg().	Agg(method='agg_sum')
heuristics	list[MinMaxHeuristic | SpacingHeuristic]	A list of formant tracking heuristics to use.	[]

Attributes

Name	Type	Description
maximum_formant	float	The max formant
time_domain	np.array	The time domain of the formant estimates
formants	np.ndarray	A (formants, time) array of values. The formants as initially estimated by praat-parselmouth
smoothed_formants	np.ndarray	The smoothed formant values, using the method passed to smoother.
parameters	np.ndarray	The smoothing parameters.
smooth_error	float	The error term between formants and smoothed formants.
file_name	str	The filename of the audio file, if set.
interval	aligned_textgrid.SequenceInterval	The textgrid interval of the sound, if set.
id	str	The interval id of the sound, if set.
group	str	The tier group name of the sound, if set.

Methods

Name	Description
spectrogram	This will plot the spectrogram and formant tracks
to_df	Output either the formant values or the formant smoothing parameters as a polars dataframe

spectrogram

OneTrack.spectrogram(**kwargs)

This will plot the spectrogram and formant tracks of a single candidate track. If a file_name is provided, it will save the plot to disk.

Parameters

Name	Type	Description	Default
formants	int	The number of formants to plot. Defaults to 3.	required
maximum_frequency	float	The frequency range the spectrogram and formants will be plotted up to. Defaults to 3500.	required
tracks	bool	Whether or not to plot the formant tracks. Defaults to True. If False, just the spectogram will be plotted.	required
dynamic_range	float	A all spectrogram values below the dynamic range. will be plotted as white. Defaults to 60.	required
figsize	tuple[float, float]	Width and height of the figure in inches. Defaults to (8,5).	required
color_scale	str	A named matplotlib color scale for the spectrogram. Defaults to “Greys”. See here for more options.	required
file_name	Path | None	If provided, how to save the spectrogram. If not provided (None) the plot will show interactively. Defaults to None.	required
dpi	float	If the plot is being saved, its image resolution in dots per inch. Defaults to 100.	required

to_df

OneTrack.to_df(output='formants')

Output either the formant values or the formant smoothing parameters as a polars dataframe

Parameters

Name	Type	Description	Default
output	Literal['formants', 'param', 'log_param']	Whether to output the formants or the smoothing parameters. Defaults to “formants”.	'formants'

Returns

Name	Type	Description
	pl.DataFrame	A polars.DataFrame