madmom.features.notes¶
This module contains note transcription related functionality.
Notes are stored as numpy arrays with the following column definition:
‘note_time’ ‘MIDI_note’ [‘duration’ [‘MIDI_velocity’]]
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class
madmom.features.notes.RNNPianoNoteProcessor(**kwargs)[source]¶ Processor to get a (piano) note activation function from a RNN.
Examples
Create a RNNPianoNoteProcessor and pass a file through the processor to obtain a note onset activation function (sampled with 100 frames per second).
>>> proc = RNNPianoNoteProcessor() >>> proc <madmom.features.notes.RNNPianoNoteProcessor object at 0x...> >>> act = proc('tests/data/audio/sample.wav') >>> act.shape (281, 88) >>> act array([[-0.00014, 0.0002 , ..., -0. , 0. ], [ 0.00008, 0.0001 , ..., 0.00006, -0.00001], ..., [-0.00005, -0.00011, ..., 0.00005, -0.00001], [-0.00017, 0.00002, ..., 0.00009, -0.00009]], dtype=float32)
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class
madmom.features.notes.NotePeakPickingProcessor(threshold=0.5, smooth=0.0, pre_avg=0.0, post_avg=0.0, pre_max=0.0, post_max=0.0, combine=0.03, delay=0.0, online=False, fps=100, **kwargs)[source]¶ This class implements the note peak-picking functionality.
Parameters: - threshold : float
Threshold for peak-picking.
- smooth : float, optional
Smooth the activation function over smooth seconds.
- pre_avg : float, optional
Use pre_avg seconds past information for moving average.
- post_avg : float, optional
Use post_avg seconds future information for moving average.
- pre_max : float, optional
Use pre_max seconds past information for moving maximum.
- post_max : float, optional
Use post_max seconds future information for moving maximum.
- combine : float, optional
Only report one note per pitch within combine seconds.
- delay : float, optional
Report the detected notes delay seconds delayed.
- online : bool, optional
Use online peak-picking, i.e. no future information.
- fps : float, optional
Frames per second used for conversion of timings.
Returns: - notes : numpy array
Detected notes [seconds, pitch].
Notes
If no moving average is needed (e.g. the activations are independent of the signal’s level as for neural network activations), pre_avg and post_avg should be set to 0. For peak picking of local maxima, set pre_max >= 1. / fps and post_max >= 1. / fps. For online peak picking, all post_ parameters are set to 0.
Examples
Create a PeakPickingProcessor. The returned array represents the positions of the onsets in seconds, thus the expected sampling rate has to be given.
>>> proc = NotePeakPickingProcessor(fps=100) >>> proc <madmom.features.notes.NotePeakPickingProcessor object at 0x...>
Call this NotePeakPickingProcessor with the note activations from an RNNPianoNoteProcessor.
>>> act = RNNPianoNoteProcessor()('tests/data/audio/stereo_sample.wav') >>> proc(act) array([[ 0.14, 72. ], [ 1.56, 41. ], [ 3.37, 75. ]])