Filterbank¶
Provides the FilterbankProcessor class to extract filterbank features
Extract mel-filterbank features from an audio signal. Use the Kaldi implementation (see [kaldi-fbank]).
Examples
>>> from shennong.audio import Audio
>>> from shennong.processor.filterbank import FilterbankProcessor
>>> audio = Audio.load('./test/data/test.wav')
Initialize the filterbank processor with some options and compute the features:
>>> processor = FilterbankProcessor(sample_rate=audio.sample_rate)
>>> processor.use_energy = False
>>> fbank = processor.process(audio)
>>> fbank.shape
(140, 23)
Using energy adds a column to the output:
>>> processor.use_energy = True
>>> fbank = processor.process(audio)
>>> fbank.shape
(140, 24)
References
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class
shennong.processor.filterbank.FilterbankProcessor(sample_rate=16000, frame_shift=0.01, frame_length=0.025, dither=1.0, preemph_coeff=0.97, remove_dc_offset=True, window_type='povey', round_to_power_of_two=True, blackman_coeff=0.42, snip_edges=True, num_bins=23, low_freq=20, high_freq=0, vtln_low=100, vtln_high=- 500, use_energy=False, energy_floor=0.0, raw_energy=True, htk_compat=False, use_log_fbank=True, use_power=True)[source]¶ Bases:
shennong.processor.base.MelFeaturesProcessorMel-filterbank features
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property
name¶ Name of the processor
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property
blackman_coeff¶ Constant coefficient for generalized Blackman window
Used only if window_type is ‘blackman’
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property
dither¶ Amount of dithering
0.0 means no dither
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property
frame_length¶ Frame length in seconds
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property
frame_shift¶ Frame shift in seconds
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get_params(deep=True)¶ Get parameters for this processor.
- Parameters
deep (boolean, optional) – If True, will return the parameters for this processor and contained subobjects that are processors. Default to True.
- Returns
params (mapping of string to any) – Parameter names mapped to their values.
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get_properties(**kwargs)¶ Return the processors properties as a dictionary
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property
high_freq¶ High cutoff frequency for mel bins in Hertz
If high_freq < 0, offset from the Nyquist frequency
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property
log¶ Processor logger
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property
low_freq¶ Low cutoff frequency for mel bins in Hertz
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property
num_bins¶ Number of triangular mel-frequency bins
The minimal number of bins is 3
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property
preemph_coeff¶ Coefficient for use in signal preemphasis
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process(signal, vtln_warp=1.0)¶ Compute features with the specified options
Do an optional feature-level vocal tract length normalization (VTLN) when vtln_warp != 1.0.
- Parameters
signal (Audio, shape = [nsamples, 1]) – The input audio signal to compute the features on, must be mono
vtln_warp (float, optional) – The VTLN warping factor to be applied when computing features. Be 1.0 by default, meaning no warping is to be done.
- Returns
features (Features, shape = [nframes, ndims]) – The computed features, output will have as many rows as there are frames (depends on the specified options frame_shift and frame_length).
- Raises
ValueError – If the input signal has more than one channel (i.e. is not mono). If sample_rate != signal.sample_rate.
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process_all(utterances, njobs=None, **kwargs)¶ Returns features processed from several input utterances
This function processes the features in parallel jobs.
- Parameters
utterances (:class`~shennong.uttterances.Utterances`) – The utterances on which to process features on.
njobs (int, optional) – The number of parallel jobs to run in background. Default to the number of CPU cores available on the machine.
**kwargs (dict, optional) – Extra arguments to be forwarded to the process method. Keys must be the same as for utterances.
- Returns
features (
FeaturesCollection) – The computed features on each input signal. The keys of output features are the keys of the input utterances.- Raises
ValueError – If the njobs parameter is <= 0 or if an entry is missing in optioanl kwargs.
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property
remove_dc_offset¶ If True, subtract mean from waveform on each frame
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property
round_to_power_of_two¶ If true, round window size to power of two
This is done by zero-padding input to FFT
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property
sample_rate¶ Waveform sample frequency in Hertz
Must match the sample rate of the signal specified in process
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set_logger(level, formatter='%(levelname)s - %(name)s - %(message)s')¶ Change level and/or format of the processor’s logger
- Parameters
level (str) – The minimum log level handled by the logger (any message above this level will be ignored). Must be ‘debug’, ‘info’, ‘warning’ or ‘error’.
formatter (str, optional) – A string to format the log messages, see https://docs.python.org/3/library/logging.html#formatter-objects. By default display level and message. Use ‘%(asctime)s - %(levelname)s - %(name)s - %(message)s’ to display time, level, name and message.
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set_params(**params)¶ Set the parameters of this processor.
- Returns
self
- Raises
ValueError – If any given parameter in
paramsis invalid for the processor.
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property
snip_edges¶ If true, output only frames that completely fit in the file
When True the number of frames depends on the frame_length. If False, the number of frames depends only on the frame_shift, and we reflect the data at the ends.
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property
use_energy¶ Add an extra dimension with energy to the filterbank output
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property
vtln_high¶ High inflection point in piecewise linear VTLN warping function
In Hertz. If vtln_high < 0, offset from high_freq
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property
vtln_low¶ Low inflection point in piecewise linear VTLN warping function
In Hertz
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property
window_type¶ Type of window
Must be ‘hamming’, ‘hanning’, ‘povey’, ‘rectangular’ or ‘blackman’
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property
energy_floor¶ Floor on energy (absolute, not relative) in filterbanks
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property
raw_energy¶ If true, compute energy before preemphasis and windowing
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property
htk_compat¶ If True, get closer to HTK filterbank features.
Put energy last.
Warning: Not sufficient to get HTK compatible features (need to change other parameters)
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property
use_log_fbank¶ If true, produce log-filterbank, else produce linear
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property
ndims¶ Dimension of the output features frames
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property
use_power¶ If true, use power, else use magnitude
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property