vocalpy.segment.MeanSquaredParams

class vocalpy.segment.MeanSquaredParams(threshold: int = 5000, min_dur: float = 0.02, min_silent_dur: float = 0.002, freq_cutoffs: ~typing.Iterable = (500, 10000), smooth_win: int = 2, scale: bool = True, scale_val: int | float = 32768, scale_dtype: type[~typing.Any] | ~numpy.dtype[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | tuple[~typing.Any, ~typing.Any] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | str | None = <class 'numpy.int16'>)

Bases: Params

Data class that represents parameters for vocalpy.segment.ava.segment().

Attributes:

thresholdint

Value above which mean squared signal is considered part of a segment. Default is 5000.

min_durfloat

Minimum duration of a segment, in seconds. Default is 0.02, i.e. 20 ms.

min_silent_durfloat

Minimum duration of silent gap between segments, in seconds. Default is 0.002, i.e. 2 ms.

freq_cutoffsIterable

Cutoff frequencies for bandpass filter. List or tuple with two elements, default is (500, 10000).

smooth_winint

Size of smoothing window in milliseconds. Default is 2.

scalebool

If True, scale the sound.data. Default is True. This is needed to replicate the behavior of evsonganaly, which assumes the audio data is loaded as 16-bit integers. Since the default for vocalpy.Sound is to load sounds with a numpy dtype of float64, this function defaults to multiplying the sound.data by 2**15, and then casting to the int16 dtype. This replicates the behavior of the evsonganaly function, given data with dtype float64. If you have loaded a sound with a dtype of int16, then set this to False.

scale_val

Value to multiply the sound.data by, to scale the data. Default is 2**15. Only used if scale is True. This is needed to replicate the behavior of evsonganaly, which assumes the audio data is loaded as 16-bit integers.

scale_dtypenumpy.dtype

Signed integer type, compatible with C short.

Methods

scale_dtype

alias of int16

keys

__init__(threshold: int = 5000, min_dur: float = 0.02, min_silent_dur: float = 0.002, freq_cutoffs: ~typing.Iterable = (500, 10000), smooth_win: int = 2, scale: bool = True, scale_val: int | float = 32768, scale_dtype: type[~typing.Any] | ~numpy.dtype[~typing.Any] | ~numpy._typing._dtype_like._SupportsDType[~numpy.dtype[~typing.Any]] | tuple[~typing.Any, ~typing.Any] | list[~typing.Any] | ~numpy._typing._dtype_like._DTypeDict | str | None = <class 'numpy.int16'>) → None

Methods

__init__([threshold, min_dur, ...])
keys()

Attributes

freq_cutoffs
min_dur
min_silent_dur
scale
scale_val
smooth_win
threshold

scale_dtype

alias of int16