#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''Instantaneous event coding'''
import numpy as np
import jams
from librosa import time_to_frames
from librosa.sequence import transition_loop, transition_cycle
from mir_eval.util import boundaries_to_intervals, adjust_intervals
from .base import BaseTaskTransformer
from ..exceptions import ParameterError
from ..labels import LabelBinarizer, LabelEncoder
__all__ = ['BeatTransformer', 'BeatPositionTransformer']
class BeatTransformer(BaseTaskTransformer):
'''Task transformation for beat tracking
Attributes
----------
name : str
The name of this transformer
sr : number > 0
The audio sampling rate
hop_length : int > 0
The hop length for annotation frames
p_self_beat : None, float in (0, 1), or np.ndarray [shape=(2,)]
Optional self-loop probability(ies), used for Viterbi decoding
p_state_beat : None or float in (0, 1)
Optional marginal probability for beat state
p_init_beat : None or float in (0, 1)
Optional initial probability for beat state
p_self_down : None, float in (0, 1), or np.ndarray [shape=(2,)]
Optional self-loop probability(ies), used for Viterbi decoding
p_state_down : None or float in (0, 1)
Optional marginal probability for downbeat state
p_init_down : None or float in (0, 1)
Optional initial probability for downbeat state
'''
def __init__(self, name='beat', sr=22050, hop_length=512,
p_self_beat=None, p_init_beat=None, p_state_beat=None,
p_self_down=None, p_init_down=None, p_state_down=None):
super(BeatTransformer, self).__init__(name=name,
namespace='beat',
sr=sr, hop_length=hop_length)
self.set_transition_beat(p_self_beat)
if p_init_beat is not None:
if not np.isscalar(p_init_beat):
raise ParameterError('Invalid p_init_beat={}'.format(p_init_beat))
self.beat_p_init = p_init_beat
if p_state_beat is not None:
if not np.isscalar(p_state_beat):
raise ParameterError('Invalid p_state_beat={}'.format(p_state_beat))
self.beat_p_state = p_state_beat
self.set_transition_down(p_self_beat)
if p_init_down is not None:
if not np.isscalar(p_init_down):
raise ParameterError('Invalid p_init_down={}'.format(p_init_down))
self.down_p_init = p_init_down
if p_state_down is not None:
if not np.isscalar(p_state_down):
raise ParameterError('Invalid p_state_down={}'.format(p_state_down))
self.down_p_state = p_state_down
self.register('beat', [None], np.bool)
self.register('downbeat', [None], np.bool)
self.register('mask_downbeat', [1], np.bool)
def set_transition_beat(self, p_self):
'''Set the beat-tracking transition matrix according to
self-loop probabilities.
Parameters
----------
p_self : None, float in (0, 1), or np.ndarray [shape=(2,)]
Optional self-loop probability(ies), used for Viterbi decoding
'''
if p_self is None:
self.beat_transition = None
else:
self.beat_transition = transition_loop(2, p_self)
def set_transition_down(self, p_self):
'''Set the downbeat-tracking transition matrix according to
self-loop probabilities.
Parameters
----------
p_self : None, float in (0, 1), or np.ndarray [shape=(2,)]
Optional self-loop probability(ies), used for Viterbi decoding
'''
if p_self is None:
self.down_transition = None
else:
self.down_transition = transition_loop(2, p_self)
def transform_annotation(self, ann, duration):
'''Apply the beat transformer
Parameters
----------
ann : jams.Annotation
The input annotation
duration : number > 0
The duration of the audio
Returns
-------
data : dict
data['beat'] : np.ndarray, shape=(n, 1)
Binary indicator of beat/non-beat
data['downbeat'] : np.ndarray, shape=(n, 1)
Binary indicator of downbeat/non-downbeat
mask_downbeat : bool
True if downbeat annotations are present
'''
mask_downbeat = False
intervals, values = ann.to_interval_values()
values = np.asarray(values)
beat_events = intervals[:, 0]
beat_labels = np.ones((len(beat_events), 1))
idx = (values == 1)
if np.any(idx):
downbeat_events = beat_events[idx]
downbeat_labels = np.ones((len(downbeat_events), 1))
mask_downbeat = True
else:
downbeat_events = np.zeros(0)
downbeat_labels = np.zeros((0, 1))
target_beat = self.encode_events(duration,
beat_events,
beat_labels)
target_downbeat = self.encode_events(duration,
downbeat_events,
downbeat_labels)
return {'beat': target_beat,
'downbeat': target_downbeat,
'mask_downbeat': mask_downbeat}
def inverse(self, encoded, downbeat=None, duration=None):
'''Inverse transformation for beats and optional downbeats'''
ann = jams.Annotation(namespace=self.namespace, duration=duration)
beat_times = np.asarray([t for t, _ in self.decode_events(encoded,
transition=self.beat_transition,
p_init=self.beat_p_init,
p_state=self.beat_p_state) if _])
beat_frames = time_to_frames(beat_times,
sr=self.sr,
hop_length=self.hop_length)
if downbeat is not None:
downbeat_times = set([t for t, _ in self.decode_events(downbeat,
transition=self.down_transition,
p_init=self.down_p_init,
p_state=self.down_p_state) if _])
pickup_beats = len([t for t in beat_times
if t < min(downbeat_times)])
else:
downbeat_times = set()
pickup_beats = 0
value = - pickup_beats - 1
for beat_t, beat_f in zip(beat_times, beat_frames):
if beat_t in downbeat_times:
value = 1
else:
value += 1
confidence = encoded[beat_f]
ann.append(time=beat_t,
duration=0,
value=value,
confidence=confidence)
return ann
class BeatPositionTransformer(BaseTaskTransformer):
'''Encode beat- and downbeat-annotations as labeled intervals.
This transformer assumes that the `value` field of a beat annotation
encodes its metrical position (1, 2, 3, 4, ...).
A `value` of 0 indicates that the beat does not belong to a bar,
and should be used to indicate pickup beats.
Beat position strings are coded as SUBDIVISION/POSITION
For example, in 4/4 time, the 2 beat would be coded as "04/02".
'''
def __init__(self, name, max_divisions=12,
sr=22050, hop_length=512, sparse=False):
super(BeatPositionTransformer, self).__init__(name=name,
namespace='beat',
sr=sr,
hop_length=hop_length)
# Make the vocab set
if not isinstance(max_divisions, int) or max_divisions < 1:
raise ParameterError('Invalid max_divisions={}'.format(max_divisions))
self.max_divisions = max_divisions
labels = self.vocabulary()
self.sparse = sparse
if self.sparse:
self.encoder = LabelEncoder()
else:
self.encoder = LabelBinarizer()
self.encoder.fit(labels)
self._classes = set(self.encoder.classes_)
# transitions should use transition_loop here
# construct block-wise for each metrical length
# initial-state distributions should be over X
# X -> **/01 s
if self.sparse:
self.register('position', [None, 1], np.int)
else:
self.register('position', [None, len(self._classes)], np.bool)
def vocabulary(self):
states = ['X']
for d in range(1, self.max_divisions + 1):
for n in range(1, d + 1):
states.append('{:02d}/{:02d}'.format(d, n))
return states
def transform_annotation(self, ann, duration):
'''Transform an annotation to the beat-position encoding
Parameters
----------
ann : jams.Annotation
The annotation to convert
duration : number > 0
The duration of the track
Returns
-------
data : dict
data['position'] : np.ndarray, shape=(n, n_labels) or (n, 1)
A time-varying label encoding of beat position
'''
# 1. get all the events
# 2. find all the downbeats
# 3. map each downbeat to a subdivision counter
# number of beats until the next downbeat
# 4. pad out events to intervals
# 5. encode each beat interval to its position
boundaries, values = ann.to_interval_values()
# Convert to intervals and span the duration
# padding at the end of track does not propagate the right label
# this is an artifact of inferring end-of-track from boundaries though
boundaries = list(boundaries[:, 0])
if boundaries and boundaries[-1] < duration:
boundaries.append(duration)
intervals = boundaries_to_intervals(boundaries)
intervals, values = adjust_intervals(intervals, values,
t_min=0,
t_max=duration,
start_label=0,
end_label=0)
values = np.asarray(values, dtype=int)
downbeats = np.flatnonzero(values == 1)
position = []
for i, v in enumerate(values):
# If the value is a 0, mark it as X and move on
if v == 0:
position.extend(self.encoder.transform(['X']))
continue
# Otherwise, let's try to find the surrounding downbeats
prev_idx = np.searchsorted(downbeats, i, side='right') - 1
next_idx = 1 + prev_idx
if prev_idx >= 0 and next_idx < len(downbeats):
# In this case, the subdivision is well-defined
subdivision = downbeats[next_idx] - downbeats[prev_idx]
elif prev_idx < 0 and next_idx < len(downbeats):
subdivision = np.max(values[:downbeats[0]+1])
elif next_idx >= len(downbeats):
subdivision = len(values) - downbeats[prev_idx]
if subdivision > self.max_divisions or subdivision < 1:
position.extend(self.encoder.transform(['X']))
else:
position.extend(self.encoder.transform(['{:02d}/{:02d}'.format(subdivision, v)]))
dtype = self.fields[self.scope('position')].dtype
position = np.asarray(position)
if self.sparse:
position = position[:, np.newaxis]
target = self.encode_intervals(duration, intervals, position,
multi=False, dtype=dtype)
return {'position': target}
def inverse(self, encoded, duration=None):
'''Inverse transformation'''
raise NotImplementedError
