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Expression_style_recognition.py
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Expression_style_recognition.py
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#!/usr/bin/env python
# encoding: utf-8
"""
Author: Yuan-Ping Chen
Data: 2016/03/10
-------------------------------------------------------------------------------
Expression style recognition: automatically recognize the electric
guitar expression style.
-------------------------------------------------------------------------------
Args:
input_files: Audio files to be processed.
Only the wav files would be considered.
output_dir: Directory for storing the results.
Optional args:
Please refer to --help.
-------------------------------------------------------------------------------
Returns:
expression_style_note: Text file of array, storing the onset, offset
and pitch of each note as well as its expression.
The file is attached with .expression_style_note
extenion.
Example:
(0) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Pit On Dur PreB B R P H S SI SO V
[ 66 1.24 0.5 2 0 0 0 0 1 2 1 1]
Pi: pitch (MIDI number)
On: onset (sec.)
Dur: duration (sec.)
PreB: pre-bend
B: string bend (0 for none,
1 for bend by 1 semitone,
2 for bend by 2 semitone,
3 for bend by 3 semitone,
R: release (0: none,
1: release by 1 semitone,
2: release by 2 semitone,
3: release by 3 semitone)
P: pull-off (0: none,
1: pull-off start,
2: pull-off stop)
H: hammer-on (0: none,
1: hammer-on start,
2: hammer-on stop)
S: legato slide (0: none,
1: legato slide start,
2: legato slide stop,
SI: slide in (0: none,
1: slide in from below,
2: slide in from above)
SO: slide out (0: none,
1: slide out downward,
2: slide out upward)
V: vibrato (0 for none,
1 for vibrato: vivrato with entext smaller or equal to 1 semitone,
2 for wild vibrato: vibrato with entext larger than 1 semitone)
"""
import glob, os
import numpy as np
import pickle
import essentia
from essentia.standard import EasyLoader, Vibrato
import Candidate_selection as CS
from Feature_extraction import extract_feature_of_audio_clip
from Classification import data_preprocessing
from GuitarTranscription_parameters import *
from GuitarTranscription_utility import note_pruning, midi2hertz
import GuitarTranscription_evaluation as GTEval
import fnmatch
class Common(object):
@staticmethod
def update_esn(expression_style_note, note_with_expression_style, technique, sub_technique):
"""
Update expression_style_note array.
:param expression_style_note: numpy array of expression_style_note.
:param note_with_expression_style: numpy array of note event with expression style.
:param technique: string of technique.
:param sub_technique: float number of sub technique.
:returns: numpy array of updated expression_style_note.
"""
if technique=='pre-bend': t = 3
elif technique=='bend': t = 4
elif technique=='release': t = 5
elif technique=='pull': t = 6
elif technique=='hamm': t = 7
elif technique=='slide': t = 8
elif technique=='slide in': t = 9
elif technique=='slide out': t = 10
elif technique=='vibrato': t = 11
note_to_be_deleted = np.empty([0])
for r_n in range(len(note_with_expression_style)):
for r_esn in range(len(expression_style_note)):
# if the onsets of expression_style_note and note_with_expression_style are equal
if note_with_expression_style[r_n,1]==expression_style_note[r_esn,1]:
# if the duration of current expression_style_note is larger than or equal to the duration of note_with_expression_style
if expression_style_note[r_esn,2]>=note_with_expression_style[r_n,2]:
expression_style_note[r_esn,2]=note_with_expression_style[r_n,2]
expression_style_note[r_esn,t]=sub_technique
else:
# loop from the next expression_style_note
for r_esn_r in range(r_esn+1,len(expression_style_note)):
expression_style_note_offset = expression_style_note[r_esn_r,1]+expression_style_note[r_esn_r,2]
note_with_expression_style_offset = note_with_expression_style[r_n,1]+note_with_expression_style[r_n,2]
# check if the offset of expression_style_note is larger than or equal to the offset of note_with_expression_style
if expression_style_note_offset>=note_with_expression_style_offset:
# the expression_style_note will not be deleted if the onset exceed the offset of note_with_expression_style, vice versa
if expression_style_note[r_esn_r,1]>note_with_expression_style_offset:
expression_style_note[r_esn,2]=note_with_expression_style[r_n,2]
expression_style_note[r_esn,t]=sub_technique
break
else:
expression_style_note[r_esn,2]=note_with_expression_style[r_n,2]
expression_style_note[r_esn,t]=sub_technique
note_to_be_deleted = np.append(note_to_be_deleted,[r_esn_r], axis=0)
break
else:
note_to_be_deleted = np.append(note_to_be_deleted,[r_esn_r], axis=0)
expression_style_note = np.delete(expression_style_note, note_to_be_deleted,axis=0)
return expression_style_note
@staticmethod
def update_ts(expression_style_ts, time_segment, technique):
"""
Update expression_style_time_segment array.
:param expression_style_ts: np.ndarray [n, 3]
array of start time, end time, technique index
:param time_segment: np.ndarray [n, 2]
array of start time, end time in seconds.
:param tech_index: int
index of detected expression style.
:returns: np.ndarray [n, 3]
updated expression_style_ts np.ndarray
"""
if technique=='pre-bend': tech_index = 3
elif technique=='bend': tech_index = 4
elif technique=='release': tech_index = 5
elif technique=='pull': tech_index = 6
elif technique=='hamm': tech_index = 7
elif technique=='slide': tech_index = 8
elif technique=='slide in': tech_index = 9
elif technique=='slide out': tech_index = 10
elif technique=='vibrato': tech_index = 11
tech = np.empty([time_segment.shape[0],1])
tech.fill(tech_index)
expression_style_ts = np.vstack([expression_style_ts, np.hstack([time_segment, tech])])
expression_style_ts = expression_style_ts[np.argsort(expression_style_ts[:,0], axis = 0)]
return expression_style_ts
def sec_2_note(self):
self.long_slide = np.empty([0,3])
for r_lss in range(len(self.long_slide_sec)):
for r_mn in range(len(self.merged_note)):
if self.long_slide_sec[r_lss,0]>self.merged_note[r_mn,1] and self.long_slide_sec[r_lss,0]<self.merged_note[r_mn,1]+self.merged_note[r_mn,2]:
long_slide_pitch = self.merged_note[r_mn,0]
long_slide_onset = self.merged_note[r_mn,1]
if r_mn+1<=len(self.merged_note):
for r_mn_r in range(r_mn+1,len(self.merged_note)):
if self.long_slide_sec[r_lss,1]>self.merged_note[r_mn_r,1] and self.long_slide_sec[r_lss,1]<self.merged_note[r_mn_r,1]+self.merged_note[r_mn_r,2]:
long_slide_offset = self.merged_note[r_mn_r,1]+self.merged_note[r_mn_r,2]
else:
long_slide_offset = self.long_slide_sec[r_lss,1]
else:
long_slide_offset = self.long_slide_sec[r_lss,1]
long_slide = [long_slide_pitch, long_slide_onset, long_slide_offset]
self.long_slide = np.append(self.long_slide, [long_slide], axis=0)
@staticmethod
def note_2_ts(note):
"""
Transform note array to time segment array.
:param: note np.ndarray [n, 3]
array of note [Pitch, onset, duration]
:return: ts np.ndarray [n, 3]
array of time segment [start, end]
"""
ts = note[:,1:3].copy()
ts[:,1] = ts[:,0]+ts[:,1]
return ts
class WildVibrato(Common):
def __init__(self):
"""
Creates a new Wav object instance of the given file.
:param filename: name of the .wav file
"""
# self.merged_note = merged_note.copy()
self.technique = 'vibrato'
self.super_wild_vibrato = None
self.wild_vibrato = None
def detect(self, raw_note):
merged_notes, self.super_wild_vibrato = WildVibrato.identify_serrated_pattern(raw_note,2)
# vibrato with extent of 1 semitone
merged_notes, self.wild_vibrato = WildVibrato.identify_serrated_pattern(merged_notes,1)
expression_style_note = np.hstack((merged_notes,np.zeros((merged_notes.shape[0],9))))
expression_style_ts = np.empty([0,3])
time_segment = Common.note_2_ts(self.super_wild_vibrato)
expression_style_ts = Common.update_ts(expression_style_ts, time_segment, technique=self.technique)
time_segment = Common.note_2_ts(self.wild_vibrato)
expression_style_ts = Common.update_ts(expression_style_ts, time_segment, technique=self.technique)
expression_style_note = Common.update_esn(expression_style_note=expression_style_note,
note_with_expression_style=self.super_wild_vibrato,
technique=self.technique,
sub_technique=2)
expression_style_note = Common.update_esn(expression_style_note=expression_style_note,
note_with_expression_style=self.wild_vibrato,
technique=self.technique,
sub_technique=2)
return expression_style_note, expression_style_ts
@staticmethod
def identify_serrated_pattern(note_pseudo,extent):
"""
Merge notes of wild vibrato by merging series of notes in serrated patter
Usage:
:param note: array of notes [pitch(MIDI#) onset(sec) duration(sec)].
:param extent: the heigh in semitone of the serrated pattern.
:returns: merged notes.
wild vibrato notes.
"""
note = note_pseudo.copy()
wild_vibrato = np.empty([0,3])
merged_notes = np.empty([0,3])
for n in range(note.shape[0]):
# if the pitch of current note is not zero
if note[n,0]!=0 and n+1<=note.shape[0]-1:
# the absolute pitch difference of current note and next note is a semitone:
# the gap of current and next note is smaller than 0.01 seconds
if note[n+1,0]-note[n,0]==extent and note[n+1,1]-(note[n,1]+note[n,2])<0.01:
pitch = note[n,0]
pitch_next = note[n+1,0]
onset_note = n
offset_note = n+1
sign = np.sign(pitch_next-pitch)
if offset_note+1<=note.shape[0]-1:
while( abs(note[offset_note+1,0]-note[offset_note,0])==extent and \
np.sign(note[offset_note+1,0]-note[offset_note,0]) != sign and \
note[offset_note+1,1]-(note[offset_note,1]+note[offset_note,2])<0.01 and \
offset_note+1<note.shape[0]-1):
sign = np.sign(note[offset_note+1,0]-note[offset_note,0])
if offset_note+1<note.shape[0]-1:
offset_note = offset_note+1
else:
break
num_notes = offset_note-onset_note+1
if num_notes>=5:
onset_time = note[onset_note,1]
duration = note[offset_note,1]+note[offset_note,2]-onset_time
merged_notes = np.append(merged_notes,[[pitch, onset_time, duration]],axis=0)
wild_vibrato = np.append(wild_vibrato,[[pitch, onset_time, duration]],axis=0)
else:
merged_notes = np.append(merged_notes,note[onset_note:offset_note+1,:],axis=0)
note[onset_note:offset_note+1,0] = 0
else:
merged_notes = np.append(merged_notes,[note[n,:]],axis=0)
elif note[n,0]!=0 and n+1>note.shape[0]-1:
merged_notes = np.append(merged_notes,[note[-1,:]],axis=0)
# append last note
return merged_notes, wild_vibrato
class LongSlide(Common):
def __init__(self, melody, hop=256, sr=44100, max_transition_note_duration=0.09, min_transition_note_duration=0.015):
"""
Creates a new Wav object instance of the given file.
:param filename: name of the .wav file
"""
self.melody = melody
self.technique = 'slide out'
self.hop = hop
self.sr = sr
self.max_transition_note_duration = max_transition_note_duration
self.min_transition_note_duration = min_transition_note_duration
self.long_slide_sec = None
self.quantised_melody = LongSlide.quantize(self.melody)
@staticmethod
def quantize(data, partitions=range(0, 90, 1), codebook=range(0, 91, 1)):
"""
Quantise array into given scale.
Usage:
index, quants = quantize([3, 34, 84, 40, 23], range(10, 90, 10), range(10, 100, 10))
>>> index
[0, 3, 8, 3, 2]
>>> quants
[10, 40, 90, 40, 30]
"""
indices = []
quantised_data = []
halfstep = float(partitions[1]-partitions[0])/2
for datum in data:
index = 0
while index < len(partitions) and datum >= partitions[index]-halfstep:
index += 1
indices.append(index-1)
quantised_data.append(codebook[index-1])
indices = np.asarray(indices)
quantised_data = np.asarray(quantised_data)
quantised_data[np.nonzero(quantised_data<0)[0]] = 0
return quantised_data
@staticmethod
def frame2note(quantised_melody,hop,sr):
"""
Convert pitch sequence into note[onset pitch duration]
:param quantised_melody: quantised pitch sequence.
:param hop: the hop size of pitch contour.
:param sr: the sampling rate of pitch contour.
:returns: note [onset pitch duration]
"""
note = np.empty([0,3])
frame = quantised_melody.copy()
for f in range(frame.shape[0]-1):
# The frame is not polyphonic and the frame is voiced
if frame[f]!=0:
pitch = frame[f]
onset = f
offset = f
while(frame[offset+1]==frame[offset] and offset+1<frame.shape[0]):
offset = offset+1
duration = offset-onset+1
note = np.append(note,[[pitch,onset,duration]],axis=0)
frame[onset:offset+1] = 0
note[:,1] = note[:,1]*hop/sr
note[:,2] = note[:,2]*hop/sr
return note
def detect(self, expression_style_note, expression_style_ts):
"""
Find long stair pattern(distance greater than three semitones) in quantised pitch sequence.
:param pitch_contour: quantised pitch sequence.
:param hop: the step size of melody contour.
:param sr: the sampling rate of melody contour.
:param max_transition_note_duration: the maximal lenght of the note in middle of the ladder.
:param min_transition_note_duration: the minimal lenght of the note in middle of the ladder.
"""
# find downward-long-stairs
# convert frame-level pitch contour into notes
self.long_slide_sec = np.empty([0,2])
note = LongSlide.frame2note(self.quantised_melody, self.hop, self.sr)
for n in range(note.shape[0]-1):
if note[n,0]!=0:
pitch = note[n,0]
onset_note = n
offset_note = n
# trace the ladder pattern
while(note[offset_note+1,0]+1==note[offset_note,0] and \
note[offset_note+1,2]>=self.min_transition_note_duration and \
note[offset_note+1,2]<=self.max_transition_note_duration and \
offset_note+2<note.shape[0]):
offset_note = offset_note+1
step = offset_note-onset_note+1
# recognized as long slide if the step number of ladder is larger than 5
if step>=5:
onset_time = note[onset_note,1]
offset_time = note[offset_note,1]+note[offset_note,2]
self.long_slide_sec = np.append(self.long_slide_sec,[[onset_time,offset_time]],axis=0)
note[onset_note:offset_note+1,0] = 0
expression_style_ts = Common.update_ts(expression_style_ts, time_segment=self.long_slide_sec, technique=self.technique)
# convert time segment of slide-out into note event
self.long_slide = self.long_slide_sec_2_long_slide(self.long_slide_sec, expression_style_note[:,0:3])
# update expression_style_note array
expression_style_note = Common.update_esn(expression_style_note=expression_style_note,
note_with_expression_style=self.long_slide,
technique=self.technique,
sub_technique=1)
return expression_style_note, expression_style_ts
@staticmethod
def long_slide_sec_2_long_slide(long_slide_sec, note):
long_slide = np.empty([0,3])
for r_lss in range(len(long_slide_sec)):
for r_mn in range(len(note)):
if long_slide_sec[r_lss,0]>note[r_mn,1] and long_slide_sec[r_lss,0]<note[r_mn,1]+note[r_mn,2]:
long_slide_pitch = note[r_mn,0]
long_slide_onset = note[r_mn,1]
if r_mn+1<=len(note):
# loop from the next note
for r_mn_r in range(r_mn+1,len(note)):
if note[r_mn_r,1]+note[r_mn_r,2]>=long_slide_sec[r_lss,1]:
if note[r_mn_r,1]>long_slide_sec[r_lss,1]:
long_slide_offset = long_slide_sec[r_lss,1]
long_slide_dur = long_slide_offset-long_slide_onset
break
else:
long_slide_offset = note[r_mn_r,1]+note[r_mn_r,2]
long_slide_dur = long_slide_offset-long_slide_onset
break
else:
long_slide_offset = long_slide_sec[r_lss,1]
long_slide_dur = long_slide_offset-long_slide_onset
long_slide_note = [long_slide_pitch, long_slide_onset, long_slide_dur]
long_slide = np.append(long_slide, [long_slide_note], axis=0)
return long_slide
def evaluate(self,answer_path):
if type(answer_path).__name__=='ndarray':
answer = answer_path.copy()
else:
answer = np.loadtxt(answer_path)
numTP = 0.
TP = np.array([])
FP = np.array([])
FN = np.array([])
estimation = self.long_slide_sec.copy()
estimation_mask = np.ones(len(self.long_slide_sec))
answer_mask = np.ones(len(answer))
for e in range(len(estimation)):
for a in range(len(answer)):
if answer[a,0]>=estimation[e,0] and answer[a,0]<=estimation[e,1]:
answer_mask[a] = 0
estimation_mask[e] = 0
numTP = numTP+1
numFN = np.sum(answer_mask)
numFP = np.sum(estimation_mask)
TP = estimation[np.nonzero(estimation_mask==0)[0]]
FP = estimation[np.nonzero(estimation_mask==1)[0]]
FN = answer[np.nonzero(answer_mask==1)[0]]
P = numTP/float(numTP+numFP)
R = numTP/float(numTP+numFN)
F = 2*P*R/float(P+R)
report.write()
return P, R, F, TP, FP, FN, numTP, numFP, numFN
class SlowBend(Common):
"""
Detect slow bend by the following rules:
i)
ii)
:param note: np.ndarray, shape=(n_event, 3)
note event[pitch(MIDI), onset, duration]
:param CAD_pattern: np.ndarray, shape=(n_event, 2)
continuous ascending/descending pattern [start, end]
:return:
"""
def __init__(self, ascending_pattern, descending_pattern):
self.technique = 'bend'
self.ascending_pattern = ascending_pattern
self.descending_pattern = descending_pattern
self.slow_bend_note = None
self.slow_release_note = None
self.short_ascending_pattern = None
self.short_descending_pattern = None
def detect(self, expression_style_note, expression_style_ts):
# detect slow bend
self.slow_bend_note, self.short_ascending_pattern = SlowBend.long_CAD_pattern_detection(expression_style_note[:,0:3], self.ascending_pattern)
# detect slow release
self.slow_release_note, self.short_descending_pattern = SlowBend.long_CAD_pattern_detection(expression_style_note[:,0:3], self.descending_pattern)
# update ts
time_segment = Common.note_2_ts(self.slow_bend_note)
expression_style_ts = Common.update_ts(expression_style_ts, time_segment, technique=self.technique)
time_segment = Common.note_2_ts(self.slow_release_note)
expression_style_ts = Common.update_ts(expression_style_ts, time_segment, technique='release')
# update esn
expression_style_note = Common.update_esn(expression_style_note, self.slow_bend_note, technique=self.technique, sub_technique = 3)
expression_style_note = Common.update_esn(expression_style_note, self.slow_release_note, technique='release', sub_technique = 3)
return expression_style_note, expression_style_ts
@staticmethod
def long_CAD_pattern_detection(note, CAD_pattern):
"""
Candidate selection for bend and slide by rules.
All the candidates must meet:
i) continuously ascending or descending pattern covers three note.
ii) The pitch difference of the three covered notes is a semitone
:param note: 2-D ndarray[pitch(MIDI). onset(s). duration(s)]
notes after mergin vibrato.
:param CAD_pattern: 1-D ndarray[onset(s). offset(s).]
continuously ascending or descending pattern.
:returns CAD_pattern: 1-D ndarray[onset(s). offset(s).]
continuously ascending or descending pattern.
:returns note_of_long_CAD: 1-D ndarray[onset(s). offset(s).]
continuously ascending or descending pattern.
"""
note_of_long_CAD = np.empty([0,3])
long_CAD_index = []
note_of_long_CAD_index = []
pseudo_CAD = CAD_pattern.copy()
pseudo_note = note.copy()
# Loop in each pattern
for p in range(pseudo_CAD.shape[0]):
onset_pattern = pseudo_CAD[p,0]
offset_pattern = pseudo_CAD[p,1]
# Loop in each note
for n in range(pseudo_note.shape[0]):
onset_note = pseudo_note[n,1]
offset_note = pseudo_note[n,1]+pseudo_note[n,2]
# Find notes where pattern located
if onset_pattern >= onset_note and onset_pattern <= offset_note:
if n+3>=pseudo_note.shape[0]:
break
for m in range(n+2,n+4):
onset_note = pseudo_note[m,1]
offset_note = pseudo_note[m,1]+pseudo_note[m,2]
if offset_pattern >= onset_note and offset_pattern <= offset_note:
if m-n>=2 and m-n<=3 and abs(pseudo_note[n,0]-pseudo_note[m,0])<=3:
pitch = pseudo_note[n,0]
onset = pseudo_note[n,1]
duration = pseudo_note[n,2]+pseudo_note[n+1,2]+pseudo_note[n+2,2]
note_of_long_CAD = np.append(note_of_long_CAD,[[pitch, onset, duration]],axis = 0)
long_CAD_index.append(p)
note_of_long_CAD_index.append(n)
note_of_long_CAD_index.append(n+1)
note_of_long_CAD_index.append(n+2)
long_CAD = pseudo_CAD[long_CAD_index,:]
short_CAD = np.delete(pseudo_CAD,long_CAD_index,axis=0)
note_of_short_CAD = np.delete(pseudo_note,note_of_long_CAD_index,axis=0)
# return note_of_long_CAD, note_of_short_CAD, long_CAD, short_CAD
return note_of_long_CAD, short_CAD
class SoftVibrato(object):
"""
Detect vibrato note-wisely
:param pitch_contour: 1-D ndarray[pitch(Hz)]
pitch contour of whole song.
:param pitch_contour_hop: int
the hop size of estimated pitch contour.
:param pitch_contour_sr: int
the sampling rate of estimated pitch contour.
:returns self.vibrato: 2-D ndarray[onset(s). offset(s).]
detected note with vibrato.
"""
def __init__(self, pitch_contour, pitch_contour_hop, pitch_contour_sr):
self.technique = 'vibrato'
self.pitch_contour = pitch_contour
self.sampleRate = pitch_contour_sr/float(pitch_contour_hop)
self.vibrato = np.empty([0,3])
def detect(self, expression_style_note, expression_style_ts):
# loop in notes
for index_note, note in enumerate(expression_style_note):
# check if vibrato is employed on the note
if expression_style_note[index_note, 11]==0:
# convert time to frame number
onset_frame = int(round(note[1]*self.sampleRate))
offset_frame = int(round((note[1]+note[2])*self.sampleRate))
# extract the pitch contour of the note
pc = self.pitch_contour[onset_frame:offset_frame]
# detect vibrato on the note
freq, extent = Vibrato(sampleRate=self.sampleRate)(essentia.array(pc))
# append the note if it's employed of vibrato
if np.count_nonzero(freq)!=0 and np.count_nonzero(extent)!=0:
self.vibrato = np.append(self.vibrato,[expression_style_note[index_note,0:3]],axis=0)
# convert vibrato note to time segment
time_segment = Common.note_2_ts(self.vibrato)
# update the result
expression_style_ts = Common.update_ts(expression_style_ts, time_segment, technique=self.technique)
expression_style_note = Common.update_esn(expression_style_note=expression_style_note,
note_with_expression_style=self.vibrato,
technique=self.technique,
sub_technique=1)
return expression_style_note, expression_style_ts
def merge_and_update_prebend_bend_release(expression_style_note, result_ref):
result = result_ref.copy()
note_to_be_deleted = np.empty([0])
for index_candi, candi_result in enumerate(result):
# if the candidate is classsified as bend
if candi_result[-1] == 0 and candi_result[0] != 0:
for index_note, note in enumerate(expression_style_note[:-1]):
# if the candidate exact covers consecutive two notes:
if candi_result[0] > note[1] and candi_result[0] < note[1]+note[2] and \
candi_result[1] > expression_style_note[index_note+1,1] and \
candi_result[1] < expression_style_note[index_note+1,1]+expression_style_note[index_note+1,2]:
current_index_candi = index_candi
current_index_note = index_note+1
while current_index_note+1 < expression_style_note.shape[0] and \
current_index_candi+1 < result.shape[0] and \
result[current_index_candi+1,2] == 0 and \
result[current_index_candi+1,0] > expression_style_note[current_index_note,1] and \
result[current_index_candi+1,0] < expression_style_note[current_index_note,1]+expression_style_note[current_index_note,2] and \
result[current_index_candi+1,1] > expression_style_note[current_index_note+1,1] and \
result[current_index_candi+1,1] < expression_style_note[current_index_note+1,1]+expression_style_note[current_index_note,2]:
current_index_candi+=1
current_index_note+=1
# delete the note which is about to be merged
note_to_be_deleted = np.append(note_to_be_deleted,range(index_note+1,current_index_note+1), axis=0)
# mark the merged candidate as 0
# if current_index_candi-index_candi > 0:
result[index_candi:current_index_candi+1, 0:2] = 0
# replace the duration of first note with the difference of the 2nd note offset and 1st note onset
expression_style_note[index_note,2]=expression_style_note[current_index_note,1]+expression_style_note[current_index_note,2]-expression_style_note[index_note,1]
# keep the predicted expression styles on merged notes which is going to be deleted
expression_style_note[index_note,6:]=np.nanmax(expression_style_note[index_note+1:current_index_note+1,6:], axis=0)
# mark the bend in expression style note
for n in range(index_note,current_index_note):
pitch_diff = expression_style_note[n+1,0]-expression_style_note[n,0]
if pitch_diff > 0:
expression_style_note[index_note,4] = pitch_diff
elif pitch_diff < 0:
expression_style_note[index_note,5] = abs(pitch_diff)
if expression_style_note[index_note, 4]==0 and expression_style_note[index_note, 5]!=0:
expression_style_note[index_note, 3] = expression_style_note[index_note, 5]
# replace the pitch of first note with the lowest pitch among index_note to current_index_note
expression_style_note[index_note,0]= np.min(expression_style_note[index_note:current_index_note+1,0])
# print note_to_be_deleted
expression_style_note = np.delete(expression_style_note, note_to_be_deleted, axis=0)
return expression_style_note
def update_pull_hamm_slide(expression_style_note, result_ref, tech_index_dic):
if tech_index_dic.has_key('pull'):
if type(tech_index_dic['pull']) is int:
pull_index_list=[tech_index_dic['pull']]
else:
pull_index_list=tech_index_dic['pull']
else:
pull_index_list=[]
if tech_index_dic.has_key('hamm'):
if type(tech_index_dic['hamm']) is int:
hamm_index_list=[tech_index_dic['hamm']]
else:
hamm_index_list=tech_index_dic['hamm']
else:
hamm_index_list=[]
if tech_index_dic.has_key('slide'):
if type(tech_index_dic['slide']) is int:
slide_index_list=[tech_index_dic['slide']]
else:
slide_index_list=tech_index_dic['slide']
else:
slide_index_list=[]
target_tech_index_list = pull_index_list+hamm_index_list+slide_index_list
result = result_ref.copy()
for index_candi, candi_result in enumerate(result):
# if the candidate is classified as target techniques
if candi_result[-1] in target_tech_index_list and candi_result[0] != 0:
for index_note, note in enumerate(expression_style_note[:-1]):
# if the candidate exact covers consecutive two notes:
if candi_result[0] > note[1] and candi_result[0] < note[1]+note[2] and \
candi_result[1] > expression_style_note[index_note+1,1] and \
candi_result[1] < expression_style_note[index_note+1,1]+expression_style_note[index_note+1,2]:
t = [k for k, v in tech_index_dic.iteritems() if v == candi_result[-1]][0]
# pull
if t=='pull':
if expression_style_note[index_note, 6]==0 and \
expression_style_note[index_note+1, 6]==0:
expression_style_note[index_note, 6]=1
expression_style_note[index_note+1, 6]=2
elif expression_style_note[index_note, 6]!=0 and \
expression_style_note[index_note+1, 6]==0:
expression_style_note[index_note+1, 6]=2
# hamm
elif t=='hamm':
if expression_style_note[index_note, 7]==0 and \
expression_style_note[index_note+1, 7]==0:
expression_style_note[index_note, 7]=1
expression_style_note[index_note+1, 7]=2
elif expression_style_note[index_note, 7]!=0 and \
expression_style_note[index_note+1, 7]==0:
expression_style_note[index_note+1, 7]=2
# slide
elif t=='slide':
expression_style_note[index_note, 8]=1
expression_style_note[index_note+1, 8]=2
return expression_style_note
def convert_index_clf_cls_2_anno_tech(cls_result, tech_index_dic):
cls_result_in_anno_index = cls_result.copy()
tech_index_dic_pseudo = tech_index_dic.copy()
answer_tech_index_dic = {'bend':4, 'release':5, 'pull':6, 'hamm':7, 'slide':8, 'vibrato':11}
if 'pull' in tech_index_dic_pseudo.keys() and 'hamm' not in tech_index_dic_pseudo.keys():
tech_index_dic_pseudo['release'] = tech_index_dic_pseudo['bend']
tech_index_dic_pseudo.pop('bend', None)
for t in tech_index_dic_pseudo:
if answer_tech_index_dic.has_key(t):
cls_result_in_anno_index[np.where(cls_result_in_anno_index[:,-1]==tech_index_dic_pseudo[t])[0],-1]=-answer_tech_index_dic[t]
cls_result_in_anno_index[:,-1] = abs(cls_result_in_anno_index[:,-1])
return cls_result_in_anno_index
def convert_double_model_index_2_single_model(cls_result, tech_index_single, tech_index_all):
# tech_index_dic_list = [{'bend':0, 'hamm':1, 'normal':2, 'slide':3},
# {'bend':0, 'normal':1, 'pull':2, 'slide':3},
# {'bend':0, 'hamm':1, 'normal':2, 'pull':3, 'slide':4}]
cls_result_in_single_model = cls_result.copy()
for t in tech_index_single:
if tech_index_all.has_key(t):
cls_result_in_single_model[np.where(cls_result_in_single_model[:,-1]==tech_index_single[t])[0],-1]=-tech_index_all[t]
cls_result_in_single_model[:,-1] = abs(cls_result_in_single_model[:,-1])
return cls_result_in_single_model
def save_esn_for_visualization(esn, output_dir, name):
np.savetxt(output_dir+os.sep+name+'.preb.esn', esn[:,[0,1,2,3]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.b.esn', esn[:,[0,1,2,4]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.r.esn', esn[:,[0,1,2,5]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.p.esn', esn[:,[0,1,2,6]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.h.esn', esn[:,[0,1,2,7]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.s.esn', esn[:,[0,1,2,8]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.si.esn', esn[:,[0,1,2,9]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.so.esn', esn[:,[0,1,2,10]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.v.esn', esn[:,[0,1,2,11]], fmt='%s')
np.savetxt(output_dir+os.sep+name+'.index.esn', np.hstack([esn[:,0:3], np.arange(esn.shape[0]).reshape(esn.shape[0],1)]), fmt='%s')
def save_cls_result_for_visualization(result_all, output_dir, name, tech_index_dic):
answer_tech_dic = {'bend':[3,4,5], 'pull':[6], 'hamm':[7], 'slide':[8,9,10], 'vibrato':[11]}
target_tech_list = [t for t in tech_index_dic if t in answer_tech_dic.keys()]
for t in target_tech_list:
if t=='bend':
np.savetxt(output_dir+os.sep+name+'.b.cls_result', result_all[np.where(result_all[:,2]==tech_index_dic[t])[0],:], fmt='%s')
elif t=='pull':
np.savetxt(output_dir+os.sep+name+'.p.cls_result', result_all[np.where(result_all[:,2]==tech_index_dic[t])[0],:], fmt='%s')
elif t=='hamm':
np.savetxt(output_dir+os.sep+name+'.h.cls_result', result_all[np.where(result_all[:,2]==tech_index_dic[t])[0],:], fmt='%s')
elif t=='slide':
np.savetxt(output_dir+os.sep+name+'.s.cls_result', result_all[np.where(result_all[:,2]==tech_index_dic[t])[0],:], fmt='%s')
elif t=='vibrato':
np.savetxt(output_dir+os.sep+name+'.v.cls_result', result_all[np.where(result_all[:,2]==tech_index_dic[t])[0],:], fmt='%s')
def parse_input_files(input_files, ext):
"""
Collect all files by given extension.
:param input_files: list of input files or directories.
:param ext: the string of file extension.
:returns: a list of stings of file name.
"""
from os.path import basename, isdir
import fnmatch
import glob
files = []
# check what we have (file/path)
if isdir(input_files):
# use all files with .raw.melody in the given path
files = fnmatch.filter(glob.glob(input_files+'/*'), '*'+ext)
else:
# file was given, append to list
if basename(input_files).find(ext)!=-1:
files.append(input_files)
print ' Input files: '
for f in files: print ' ', f
return files
def parser():
"""
Parses the command line arguments.
"""
import argparse
# define parser
p = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description="""
If invoked without any parameters, the software S1 Extract melody contour,
track notes and timestmaps of intersection of ad continuous pitch sequence
inthe given files, the pipeline is as follows,
S1.1 Extract melody contour
S1.2 Note tracking
S1.3 Find continuously ascending/descending (CAD) F0 sequence patterns
S1.4 Find intersection of note and pattern
(Candidate selection of {bend,slide,pull-off,hammer-on,normal})
""")
# general options
p.add_argument('input_audios', type=str, metavar='input_audios',
help='audio files to be processed')
p.add_argument('input_melody', type=str, metavar='input_melody',
help='melody contours to be processed')
p.add_argument('input_note', type=str, metavar='input_note',
help='note events to be processed')
p.add_argument('input_model', nargs='+', type=str, metavar='input_model',
help='pre-trained classifier')
p.add_argument('output_dir', type=str, metavar='output_dir',
help='output directory.')
p.add_argument('-p', '--prunning_note', dest='p',
help="the minimum duration of note event.", default=0.1)
# set the scaler path
p.add_argument('-scaler_path', '--scaler_path', nargs='+', type=str, metavar='scaler_path',
help="path of pre-trained scaler path.", default=None)
# set the PCA path
p.add_argument('-PCA_path', '--PCA_path', nargs='+', type=str, metavar='PCA_path',
help="path of pre-trained PCA.", default=None)
# debug
p.add_argument('-debug', dest='debug', default=None, action='store_true',
help='result data to file for debugging.')
# classification evaluation
p.add_argument('-eval_cls', '--evaluation_classification', type=str, default=None, dest='eval_cls',
help='Conduct classfication evaluation. The followed argument is parent directory of time-stamp annotation.')
# expression style time segment evaluation
p.add_argument('-eval_ts', '--evaluation_expression_style_ts', type=str, default=None, dest='eval_ts',
help='Conduct time segment-level expression style evaluation. The followed argument is parent directory of time-stamp annotation.')
# expression style note evaluation
eval_esn = p.add_argument_group('Expression style recognition evaluation arguments')
eval_esn.add_argument('-eval_esn', '--evaluation_expression_style_note', type=str, default=None, dest='eval_esn',
help='Conduct note-level expression style evaluation. The followed argument is parent directory of annotation.')
# note evaluation
eval_note = p.add_argument_group('Note evulation arguments')
eval_note.add_argument('-eval_note', '--evaluation_note', type=str, default=None, dest='eval_note',
help='Conduct note evaluation. The followed argument is parent directory of annotation.')
eval_note.add_argument('-poly_mask', '--polyphony_mask', type=str, default=None, dest='poly_mask',
help='Path of polyphonic notes mask.')
eval_note.add_argument('-onset_tol', '--onset_tolerance_window', type=float, dest='onset_tol', default=0.05,
help='Window lenght of onset tolerance. (default: %(default)s)')
eval_note.add_argument('-offset_rat', '--offset_tolerance_ratio', type=float, dest='offset_rat', default=0.2,
help='Window lenght of onset tolerance. (default: %(default)s)')
# print
p.add_argument('-v', dest='verbose', default=None, action='store_true',
help='be verbose')
# version
p.add_argument('--version', action='version',
version='%(prog)spec 1.03 (2016-05-04)')
# parse arguments
args = p.parse_args()
# print arguments
if args.verbose:
print args
# return args
return args
def main(args):
print '======================================='
print 'Running expression style recognition...'
print '======================================='
# parse and list files to be processed
audio_files = parse_input_files(args.input_audios, ext='.wav')
# create result directory
if not os.path.exists(args.output_dir): os.makedirs(args.output_dir)
print ' Output directory: ', '\n', ' ', args.output_dir
if args.debug:
if not os.path.exists(args.output_dir+os.sep+'debug'): os.makedirs(args.output_dir+os.sep+'debug')
for f in audio_files:
ext = os.path.basename(f).split('.')[-1]
name = os.path.basename(f).split('.')[0]
# load melody
melody_path = args.input_melody+os.sep+name+'.MIDI.smooth.melody'
try:
MIDI_smooth_melody = np.loadtxt(melody_path)
except IOError:
print 'The melody contour of ', name, ' doesn\'t exist!'
raw_melody_path = args.input_melody+os.sep+name+'.raw.melody'
try:
raw_melody = np.loadtxt(raw_melody_path)
except IOError:
print 'The melody contour of ', name, ' doesn\'t exist!'
# load raw note
note_path = args.input_note+os.sep+name+'.raw.note'
try:
raw_note = np.loadtxt(note_path)
except IOError:
print 'The note event of ', name, ' doesn\'t exist!'
if args.eval_note:
annotation_note = np.loadtxt(args.eval_note+os.sep+name+'.note.answer')
GTEval.evaluation_note(annotation_note, raw_note, args.output_dir, name,
onset_tolerance=args.onset_tol, offset_ratio=args.offset_rat, mode='w',
string='Raw note events',
poly_mask=args.poly_mask, extension='.csv')
"""
=====================================================================================
S.1 Detect {wild vibrato} by recognizing the serrated pattern in note events.
=====================================================================================
"""
print 'Detecting {wild vibrato}...'
WV = WildVibrato()
expression_style_note, expression_style_ts = WV.detect(raw_note)
if args.debug:
print ' Restoring results for debugging...'
# create result directory
debug_dir = args.output_dir+os.sep+'debug'+os.sep+'after_S.1_Wild_vibrato_detection'
if not os.path.exists(debug_dir):
os.makedirs(debug_dir)
# save expression_style_note
np.savetxt(debug_dir+os.sep+name+'.super_wild_vibrato',WV.super_wild_vibrato, fmt='%s')
np.savetxt(debug_dir+os.sep+name+'.wild_vibrato',WV.wild_vibrato, fmt='%s')
np.savetxt(debug_dir+os.sep+name+'.esn', expression_style_note, fmt='%s')
np.savetxt(debug_dir+os.sep+name+'.ts', expression_style_ts, fmt='%s')
save_esn_for_visualization(expression_style_note, debug_dir, name)
if args.eval_esn:
print ' Evaluating note-level expression style...'
annotation_esn = np.loadtxt(args.eval_esn+os.sep+name+'.esn.answer')
GTEval.evaluation_esn(annotation_esn, expression_style_note, args.output_dir, name, onset_tolerance=0.05, offset_ratio=0.2,
string='After wild vibrato detection', mode='w',
poly_mask=args.poly_mask, extension='.csv')
if args.eval_ts:
print ' Evaluating time segment-level expression style...'
annotation_ts = np.loadtxt(args.eval_ts+os.sep+name+'.ts.answer')
GTEval.evaluation_ts(annotation_ts, expression_style_ts, args.output_dir, name,
string='After wild vibrato detection', mode='w',
poly_mask=args.poly_mask, extension='.csv')
if args.eval_note:
print ' Evaluating note accuracy...'
# load note answer
annotation = np.loadtxt(args.eval_note+os.sep+name+'.note.answer')
note = expression_style_note[:,0:3]
# pruned_note = note_pruning(note, threshold=args.p)
GTEval.evaluation_note(annotation, note, args.output_dir, name,
onset_tolerance=args.onset_tol, offset_ratio=args.offset_rat,
string='After wild vibrato detection.', mode='a',
poly_mask=args.poly_mask, extension='.csv')
"""
================================================================================================
S.2 Detect {slide in} {slide out} by recognizing the ladder pattern in quantised melody contour.
================================================================================================
"""
print 'Detecting {slide in} {slide out} ...'
LS = LongSlide(MIDI_smooth_melody, hop=contour_hop, sr=contour_sr,
max_transition_note_duration=max_transition_note_duration,
min_transition_note_duration=min_transition_note_duration)
expression_style_note, expression_style_ts = LS.detect(expression_style_note, expression_style_ts)
if args.debug:
print ' Restoring results for debugging...'
# create result directory
debug_dir = args.output_dir+os.sep+'debug'+os.sep+'after_S.2_Slide_in_slide_out_detection'