GammatoneFilterBank

class ketos.audio.gammatone.GammatoneFilterBank(data, rate, freqs, filename=None, offset=0, label=None, annot=None, weight_func=True, **kwargs)[source]

Gammatone filter bank.

The filtered signals are stored in a 2D numpy array, where the first axis (0) is the time dimension and the second axis (1) is the frequency dimension.

Args:

data: 2d numpy array: Filtered data
rate: float: Sampling rate in Hz
freqs: array-like: Center frequencies of the filter bank in Hz
filename: str or list(str): Name of the source audio file, if available.
offset: float or array-like: Position in seconds of the left edge of the spectrogram within the source audio file, if available.
label: int: Spectrogram label. Optional
annot: AnnotationHandler: AnnotationHandler object. Optional
weight_func: bool: Apply C weighting function. Default is True.

Attributes:

data: 2d numpy array: Filtered data
rate: float: Sampling rate in Hz
freqs: array-like: Center frequencies of the filter bank in Hz
filename: str or list(str): Name of the source audio file, if available.
offset: float or array-like: Position in seconds of the left edge of the spectrogram within the source audio file, if available.
label: int: Spectrogram label. Optional
annot: AnnotationHandler: AnnotationHandler object. Optional
weight_func: bool: Apply C weighting function.

Methods

`empty`()	Creates an empty GammatoneFilterBank object
`from_wav`(path[, num_chan, freq_min, ...])	Create a Gammatone Filter Bank directly from wav file.
`from_waveform`(audio[, num_chan, freq_min, ...])	Create a Gammatone Filter Bank from an instance of `audio_signal.Waveform`.
`get_repres_attrs`()	Get audio representation attributes
`plot`(filter_id[, show_annot, figsize, ...])	Plot the filtered signal with proper axes ranges and labels.

classmethod empty()[source]: Creates an empty GammatoneFilterBank object

classmethod from_wav(path, num_chan=20, freq_min=1, channel=0, rate=None, offset=0, duration=None, resample_method='scipy', id=None, normalize_wav=False, weight_func=True, **kwargs)[source]

Create a Gammatone Filter Bank directly from wav file.

The arguments offset and duration can be used to select a portion of the wav file.

Note that values specified for the arguments offset and duration may be subject to slight adjustments to ensure that the selected portion corresponds to an integer number of samples.

Args:

path: str

Path to wav file

num_chan: int

Number of channels in the filter bank

freq_min: float

Minimum frequency of the filter bank in Hz

channel: int

Channel to read from. Only relevant for stereo recordings

rate: float

Desired sampling rate in Hz. If None, the original sampling rate will be used

offset: float

Start time of selection in seconds, relative the start of the wav file.

duration: float

Length of selection in seconds.

resample_method: str

Resampling method. Only relevant if rate is specified. Options are

kaiser_best
kaiser_fast
scipy (default)
polyphase

See https://librosa.github.io/librosa/generated/librosa.core.resample.html for details on the individual methods.

id: str

Unique identifier (optional). If None, the filename will be used.

normalize_wav: bool

Normalize the waveform to have a mean of zero (mean=0) and a standard deviation of unity (std=1) before computing the spectrogram. Default is False.

weight_func: bool

Apply C weighting function. Default is True.

Returns:

: GammatoneFilterBank: Gammatone filter bank

Example:

>>> # load gammatone filter bank from wav file
>>> from ketos.audio.gammatone import GammatoneFilterBank
>>> gfb = GammatoneFilterBank.from_wav('ketos/tests/assets/grunt1.wav', num_chan=20, freq_min=10, rate=1000)
>>> # print the center frequencies rounded to 1 decimal
>>> print(np.round(gfb.freqs,1))
[ 10.   23.7  38.2  53.5  69.7  86.8 104.9 124.1 144.3 165.7 188.4 212.3
 237.6 264.4 292.7 322.6 354.2 387.7 423.1 460.5]
>>> # display the 4th filter bank signal
>>> fig = gfb.plot(filter_id=3)
>>> fig.savefig("ketos/tests/assets/tmp/gfb3_grunt1.png")
>>> plt.close(fig)

classmethod from_waveform(audio, num_chan=20, freq_min=1, weight_func=True)[source]

Create a Gammatone Filter Bank from an instance of audio_signal.Waveform.

Args:

audio: Waveform: Audio signal
num_chan: int: Number of channels in the filter bank
freq_min: float: Minimum frequency of the filter bank in Hz
weight_func: bool: Apply C weighting function. Default is True.

Returns:

gfb: GammatoneFilterBank: Gammatone filter bank

get_repres_attrs()[source]: Get audio representation attributes

plot(filter_id, show_annot=False, figsize=(5, 4), label_in_title=True, show_envelope=False)[source]

Plot the filtered signal with proper axes ranges and labels.

Optionally, also display annotations as boxes superimposed on the signal.

Note: The resulting figure can be shown (fig.show()) or saved (fig.savefig(file_name))

Args:

filter_id: int: Filter to be plotted.
show_annot: bool: Display annotations
figsize: tuple: Figure size
label_in_title: bool: Include label (if available) in figure title
show_envelope: bool: Display envelope on top of signal

Returns:

: matplotlib.figure.Figure: A figure object.

Example:

>>> from ketos.audio.gammatone import GammatoneFilterBank
>>> # load gammatone filter bank
>>> gfb = GammatoneFilterBank.from_wav('ketos/tests/assets/grunt1.wav', num_chan=20, freq_min=10, rate=1000)
>>> # add an annotation
>>> gfb.annotate(start=1.2, end=1.6, freq_min=70, freq_max=600, label=1)
>>> # show the 4th filter bank with annotation box
>>> fig = gfb.plot(filter_id=3, show_annot=True)
>>> fig.savefig("ketos/tests/assets/tmp/gfb3_w_annot_box.png")
>>> plt.close(fig)