class_docstr: Extract learned features for chord recognition, as described in [1]_.
  
  References
  ----------
  .. [1] Filip Korzeniowski and Gerhard Widmer,
         "A Fully Convolutional Deep Auditory Model for Musical Chord
         Recognition",
         Proceedings of IEEE International Workshop on Machine Learning for
         Signal Processing (MLSP), 2016.
  
  Examples
  --------
  >>> proc = CNNChordFeatureProcessor()
  >>> proc  # doctest: +ELLIPSIS
  <madmom.features.chords.CNNChordFeatureProcessor object at 0x...>
  >>> features = proc('tests/data/audio/sample2.wav')
  >>> features.shape
  (41, 128)
  >>> features # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
  array([[0.05798, 0.     , ..., 0.02757, 0.014  ],
         [0.06604, 0.     , ..., 0.02898, 0.00886],
         ...,
         [0.00655, 0.1166 , ..., 0.00651, 0.     ],
         [0.01476, 0.11185, ..., 0.00287, 0.     ]])
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code: def __init__(self, **kwargs):
      from ..audio.signal import SignalProcessor, FramedSignalProcessor
      from ..audio.stft import ShortTimeFourierTransformProcessor
      from ..audio.spectrogram import (
          LogarithmicFilteredSpectrogramProcessor,
      )
      from ..ml.nn import NeuralNetwork
      from ..models import CHORDS_CNN_FEAT
  
      # spectrogram computation
      sig = SignalProcessor(num_channels=1, sample_rate=44100)
      frames = FramedSignalProcessor(frame_size=8192, fps=10)
      stft = ShortTimeFourierTransformProcessor()  # caching FFT window
      spec = LogarithmicFilteredSpectrogramProcessor(
          num_bands=24, fmin=60, fmax=2600, unique_filters=True
      )
  
      # padding, neural network and global average pooling
      pad = _cnncfp_pad
      nn = NeuralNetwork.load(CHORDS_CNN_FEAT[0])
      superframes = _cnncfp_superframes
      avg = _cnncfp_avg
  
      # create processing pipeline
      super(CNNChordFeatureProcessor, self).__init__(
          [sig, frames, stft, spec, pad, nn, superframes, avg]
      )
  
file_path: madmom/madmom/features/chords.py
imports: from __future__ import absolute_import, division, print_function
  from functools import partial
  import numpy
  from ..io import SEGMENT_DTYPE
  from ..processors import SequentialProcessor
label_desc: Use image node_id 1 to create a new CNNChordFeatureProcessor object from inherited base classes: SequentialProcessor with example: obj = CNNChordFeatureProcessor()