pumpp.feature.HCQT

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