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’]]

madmom.features.notes.load_notes(*args, **kwargs)

Load the notes from a file.

Parameters:

filename : str or file handle

Input file to load the notes from.

Returns:

numpy array

Notes.

Notes

The file format must be (duration and velocity being optional):

‘note_time’ ‘MIDI_note’ [‘duration’ [‘MIDI_velocity’]]

with one note per line and individual fields separated by whitespace.

madmom.features.notes.expand_notes(notes, duration=0.6, velocity=100)

Expand the notes to include all columns.

Parameters:

notes : numpy array, shape (num_notes, 2)

Notes, one per row (column definition see notes).

duration : float, optional

Note duration if not defined by notes.

velocity : int, optional

Note velocity if not defined by notes.

Returns:

numpy array

Notes (including note duration and velocity).

Notes

The note columns format must be (duration and velocity being optional):

‘note_time’ ‘MIDI_note’ [‘duration’ [‘MIDI_velocity’]]

madmom.features.notes.write_notes(notes, filename, fmt=None, delimiter='\t', header='')

Write the notes to a file (as many columns as given).

Parameters:

notes : numpy array, shape (num_notes, 2)

Notes, one per row (column definition see notes).

filename : str or file handle

Output filename or handle.

fmt : list, optional

Format of the fields (i.e. columns, see notes)

delimiter : str, optional

String or character separating the columns.

header : str, optional

Header to be written (as a comment).

Returns:

numpy array

Notes.

Notes

The note columns format must be (duration and velocity being optional):

‘note_time’ ‘MIDI_note’ [‘duration’ [‘MIDI_velocity’]]

madmom.features.notes.write_midi(notes, filename, duration=0.6, velocity=100)

Write the notes to a MIDI file.

Parameters:

notes : numpy array, shape (num_notes, 2)

Notes, one per row (column definition see notes).

filename : str

Output MIDI file.

duration : float, optional

Note duration if not defined by notes.

velocity : int, optional

Note velocity if not defined by notes.

Returns:

numpy array

Notes (including note length and velocity).

Notes

The note columns format must be (duration and velocity being optional):

‘note_time’ ‘MIDI_note’ [‘duration’ [‘MIDI_velocity’]]

madmom.features.notes.write_mirex_format(notes, filename, duration=0.6)

Write the frequencies of the notes to file (in MIREX format).

Parameters:

notes : numpy array, shape (num_notes, 2)

Notes, one per row (column definition see notes).

filename : str or file handle

Output filename or handle.

duration : float, optional

Note duration if not defined by notes.

Returns:

numpy array

Notes in MIREX format.

Notes

The note columns format must be (duration and velocity being optional):

‘note_time’ ‘MIDI_note’ [‘duration’ [‘MIDI_velocity’]]

The output format required by MIREX is:

‘onset_time’ ‘offset_time’ ‘note_frequency’

class madmom.features.notes.RNNPianoNoteProcessor(**kwargs)

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)

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)

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([ 0.09,  0.29,  0.45,  ...,  2.34,  2.49,  2.67])

process(activations, **kwargs)

Detect the notes in the given activation function.

Parameters:

activations : numpy array

Note activation function.

Returns:

onsets : numpy array

Detected notes [seconds, pitches].