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’
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__init__
(name, sr, hop_length, n_octaves=8, over_sample=3, fmin=None, harmonics=None, log=False, conv='channels_last')[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(name, sr, hop_length[, n_octaves, …])Initialize self. 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])Register a field as a tensor with specified shape and type. scope
(key)Apply the name scope to a key transform
(y, sr)Transform an audio signal transform_audio
(y)Compute the HCQT Attributes
idx