pumpp.feature.HCQT¶
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class
pumpp.feature.
HCQT
(name, sr, hop_length, n_octaves=8, over_sample=3, fmin=None, harmonics=None, log=False, conv='channels_last')[source]¶ Harmonic Constant-Q transform
Attributes
name (str) The name for this feature extractor sr (number > 0) The sampling rate of audio hop_length (int > 0) The number of samples between CQT frames n_octaves (int > 0) The number of octaves in the CQT over_sample (int > 0) The amount of frequency oversampling (bins per semitone) fmin (float > 0) The minimum frequency of the CQT harmonics (list of int >= 1) The list of harmonics to compute log (boolean) If True, scale the magnitude to decibels Otherwise, use linear magnitude conv ({‘tf’, ‘th’, ‘channels_last’, ‘channels_first’, None}) convolution dimension ordering: - ‘channels_last’ for tensorflow-style 2D convolution - ‘tf’ equivalent to ‘channels_last’ - ‘channels_first’ for theano-style 2D convolution - ‘th’ equivalent to ‘channels_first’ -
__init__
(name, sr, hop_length, n_octaves=8, over_sample=3, fmin=None, harmonics=None, log=False, conv='channels_last')[source]¶
Methods
__init__
(name, sr, hop_length[, n_octaves, ...])layers
()Construct Keras input layers for the given transformer merge
(data)Merge an array of output dictionaries into a single dictionary with properly scoped names. n_frames
(duration)Get the number of frames for a given duration phase_diff
(phase)Compute the phase differential along a given axis pop
(field)register
(key, dimension, dtype[, channels])scope
(key)Apply the name scope to a key transform
(y, sr)Transform an audio signal transform_audio
(y)Compute the HCQT Attributes
idx
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