BaseAudioTime
- class ketos.audio.base_audio.BaseAudioTime(data, time_res, filename='', offset=0, label=None, annot=None, transforms=None, transform_log=None, **kwargs)[source]
Parent class for time-series audio classes such as
audio.waveform.Waveformandaudio.spectrogram.Spectrogram.- Args:
- data: numpy array
Data
- time_res: float
Time resolution in seconds
- filename: str
Filename of the original data file, if available (optional)
- offset: float
Position within the original data file, in seconds measured from the start of the file. Defaults to 0 if not specified.
- label: int
Spectrogram label. Optional
- annot: AnnotationHandler
AnnotationHandler object. Optional
- transforms: list(dict)
List of dictionaries, where each dictionary specifies the name of a transformation and its arguments, if any. For example, {“name”:”normalize”, “mean”:0.5, “std”:1.0}
- Attributes:
- data: numpy array
Data
- ndim: int
Dimensionality of data.
- time_ax: LinearAxis
Axis object for the time dimension
- filename: str
Filename of the original data file, if available (optional)
- offset: float
Position within the original data file, in seconds measured from the start of the file. Defaults to 0 if not specified.
- label: int
Data label.
- annot: AnnotationHandler or pandas DataFrame
AnnotationHandler object.
- allowed_transforms: dict
Transforms that can be applied via the apply_transform method
- transform_log: list
List of transforms that have been applied to this object
Methods
crop([start, end, length, make_copy])Crop audio signal.
duration()Data array duration in seconds
Get instance attributes
Get audio representation attributes
label_array(label)Get an array indicating presence/absence (1/0) of the specified annotation label for each time bin.
plot([figsize, label_in_title, append_title])Plot the data with proper axes ranges and labels.
segment(window[, step])Divide the time axis into segments of uniform length, which may or may not be overlapping.
time_res()Get the time resolution.
- crop(start=None, end=None, length=None, make_copy=False)[source]
Crop audio signal.
- Args:
- start: float
Start time in seconds, measured from the left edge of spectrogram.
- end: float
End time in seconds, measured from the left edge of spectrogram.
- length: int
Horizontal size of the cropped image (number of pixels). If provided, the end argument is ignored.
- make_copy: bool
Return a cropped copy of the spectrogra. Leaves the present instance unaffected. Default is False.
- Returns:
- a: BaseAudio
Cropped data array
- label_array(label)[source]
Get an array indicating presence/absence (1/0) of the specified annotation label for each time bin.
- Args:
- label: int
Label of interest.
- Returns:
- y: numpy.array
Label array
- plot(figsize=(5, 4), label_in_title=True, append_title='')[source]
Plot the data with proper axes ranges and labels.
Optionally, also display annotations as boxes superimposed on the data.
Note: The resulting figure can be shown (fig.show()) or saved (fig.savefig(file_name))
- Args:
- figsize: tuple
Figure size
- label_in_title: bool
Include label (if available) in figure title
- append_title: str
Append this string to the title
- Returns:
- fig: matplotlib.figure.Figure
A figure object.
- ax: matplotlib.axes.Axes
Axes object
- segment(window, step=None)[source]
Divide the time axis into segments of uniform length, which may or may not be overlapping.
Window length and step size are converted to the nearest integer number of time steps.
If necessary, the data array will be padded with zeros at the end to ensure that all segments have an equal number of samples.
- Args:
- window: float
Length of each segment in seconds.
- step: float
Step size in seconds.
- Returns:
- : list(BaseAudioTime)
Stacked data segments