def estimate(self,
pitch_track,
mode_names=[],
mode_name='',
mode_dir='./',
est_tonic=True,
est_mode=True,
rank=1,
distance_method="euclidean",
metric='pcd',
ref_freq=440):
### Preliminaries before the estimations
cent_track = mf.hz_to_cent(pitch_track, ref_freq)
dist = mf.generate_pd(cent_track,
ref_freq=ref_freq,
smooth_factor=self.smooth_factor,
cent_ss=self.cent_ss)
dist = mf.generate_pcd(dist) if (metric == 'pcd') else dist
mode_collections = [
self.load_collection(mode, metric, dist_dir=mode_dir)
for mode in mode_names
]
mode_dists = [dist for col in mode_collections for dist in col]
mode_dist = self.load_collection(
mode_name, metric,
dist_dir=mode_dir) if (mode_name != '') else None
tonic_list = np.zeros(rank)
mode_list = ['' for x in range(rank)]
if (est_tonic):
if (metric == 'pcd'):
### Shifting to the global minima to prevent wrong detection of peaks
shift_factor = dist.vals.tolist().index(min(dist.vals))
dist = dist.shift(shift_factor)
anti_freq = mf.cent_to_hz([dist.bins[shift_factor]],
ref_freq=ref_freq)[0]
peak_idxs, peak_vals = dist.detect_peaks()
elif (metric == 'pd'):
peak_idxs, peak_vals = dist.detect_peaks()
origin = np.where(dist.bins == 0)[0][0]
shift_idxs = [(idx - origin) for idx in peak_idxs]
### Call to actual estimation functions
if (est_tonic and est_mode):
if (metric == 'pcd'):
dist_mat = mf.generate_distance_matrix(dist,
peak_idxs,
mode_dists,
method=distance_method)
for r in range(rank):
min_row = np.where((dist_mat == np.amin(dist_mat)))[0][0]
min_col = np.where((dist_mat == np.amin(dist_mat)))[1][0]
tonic_list[r] = mf.cent_to_hz(
[dist.bins[peak_idxs[min_row]]], anti_freq)[0]
mode_list[r] = (mode_dists[min_col].source,
mode_dists[min_col].segmentation)
dist_mat[min_row][min_col] = (np.amax(dist_mat) + 1)
return mode_list, tonic_list
elif (metric == 'pd'):
dist_mat = np.zeros((len(shift_idxs), len(mode_dists)))
for m in range(len(mode_dists)):
dist_mat[:, m] = self.tonic_estimate(
dist,
shift_idxs,
mode_dists[m],
distance_method=distance_method,
metric=metric)
for r in range(rank):
min_row = np.where((dist_mat == np.amin(dist_mat)))[0][0]
min_col = np.where((dist_mat == np.amin(dist_mat)))[1][0]
tonic_list[r] = mf.cent_to_hz(
[shift_idxs[min_row] * self.cent_ss], ref_freq)[0]
mode_list[r] = (mode_dists[min_col].source,
mode_dists[min_col].segmentation)
dist_mat[min_row][min_col] = (np.amax(dist_mat) + 1)
return mode_list, tonic_list
elif (est_tonic):
if (metric == 'pcd'):
dist_mat = [(np.array(
mf.generate_distance_matrix(dist,
peak_idxs, [d],
method=distance_method))[:, 0])
for d in mode_dist]
elif (metric == 'pd'):
peak_idxs = shift_idxs
temp = p_d.PitchDistribution(dist.bins,
dist.vals,
kernel_width=dist.kernel_width,
source=dist.source,
ref_freq=dist.ref_freq,
segment=dist.segmentation)
dist_mat = []
for d in mode_dist:
temp, d = mf.pd_zero_pad(temp, d, cent_ss=self.cent_ss)
### Filling both sides of vals with zeros, to make sure that the shifts won't drop any non-zero values
temp.vals = np.concatenate(
(np.zeros(abs(max(peak_idxs))), temp.vals,
np.zeros(abs(min(peak_idxs)))))
d.vals = np.concatenate(
(np.zeros(abs(max(peak_idxs))), d.vals,
np.zeros(abs(min(peak_idxs)))))
cur_vector = np.array(
mf.generate_distance_matrix(temp,
peak_idxs, [d],
method=distance_method))[:,
0]
dist_mat.append(cur_vector)
for r in range(rank):
min_row = np.where((dist_mat == np.amin(dist_mat)))[0][0]
min_col = np.where((dist_mat == np.amin(dist_mat)))[1][0]
tonic_list[r] = mf.cent_to_hz([dist.bins[peak_idxs[min_row]]],
anti_freq)[0]
dist_mat[min_row][min_col] = (np.amax(dist_mat) + 1)
return tonic_list
elif (est_mode):
distance_vector = self.mode_estimate(
dist,
mode_dists,
distance_method=distance_method,
metric=metric)
for r in range(rank):
idx = np.argmin(distance_vector)
mode_list[r] = (mode_dists[idx].source,
mode_dists[idx].segmentation)
distance_vector[idx] = (np.amax(distance_vector) + 1)
return mode_list
else:
return 0
def estimate(self,
pitch_track,
mode_names=[],
mode_name='',
mode_dir='./',
est_tonic=True,
est_mode=True,
rank=1,
distance_method="euclidean",
metric='pcd',
ref_freq=440):
"""---------------------------------------------------------------------------------------
This is the high level function that users are expected to interact with, for estimation
purposes. Using the est_* flags, it is possible to estimate tonic, mode or both.
---------------------------------------------------------------------------------------"""
### Preliminaries before the estimations
cent_track = mf.hz_to_cent(pitch_track, ref_freq=ref_freq)
dist = mf.generate_pd(cent_track,
ref_freq=ref_freq,
smooth_factor=self.smooth_factor,
cent_ss=self.cent_ss)
dist = mf.generate_pcd(dist) if (metric == 'pcd') else dist
mode_dists = [(p_d.load((m + '_' + metric + '.json'), mode_dir))
for m in mode_names]
mode_dist = p_d.load(
(mode_name + '_' + metric +
'.json'), mode_dir) if (mode_name != '') else None
tonic_list = np.zeros(rank)
mode_list = ['' for x in range(rank)]
if (est_tonic):
if (metric == 'pcd'):
### Shifting to the global minima to prevent wrong detection of peaks
shift_factor = dist.vals.tolist().index(min(dist.vals))
dist = dist.shift(shift_factor)
anti_freq = mf.cent_to_hz([dist.bins[shift_factor]],
ref_freq=ref_freq)[0]
peak_idxs, peak_vals = dist.detect_peaks()
elif (metric == 'pd'):
peak_idxs, peak_vals = dist.detect_peaks()
origin = np.where(dist.bins == 0)[0][0]
shift_idxs = [(idx - origin) for idx in peak_idxs]
### Call to actual estimation functions
if (est_tonic and est_mode):
if (metric == 'pcd'):
dist_mat = mf.generate_distance_matrix(dist,
peak_idxs,
mode_dists,
method=distance_method)
for r in range(rank):
min_row = np.where((dist_mat == np.amin(dist_mat)))[0][0]
min_col = np.where((dist_mat == np.amin(dist_mat)))[1][0]
tonic_list[r] = mf.cent_to_hz(
[dist.bins[peak_idxs[min_row]]], anti_freq)[0]
mode_list[r] = mode_names[min_col]
dist_mat[min_row][min_col] = (np.amax(dist_mat) + 1)
return mode_list, tonic_list
elif (metric == 'pd'):
dist_mat = np.zeros((len(shift_idxs), len(mode_dists)))
for m in range(len(mode_dists)):
dist_mat[:, m] = self.tonic_estimate(
dist,
shift_idxs,
mode_dists[m],
distance_method=distance_method,
metric=metric)
for r in range(rank):
min_row = np.where((dist_mat == np.amin(dist_mat)))[0][0]
min_col = np.where((dist_mat == np.amin(dist_mat)))[1][0]
tonic_list[r] = mf.cent_to_hz(
[shift_idxs[min_row] * self.cent_ss], ref_freq)[0]
mode_list[r] = mode_names[min_col]
dist_mat[min_row][min_col] = (np.amax(dist_mat) + 1)
return mode_list, tonic_list
elif (est_tonic):
if (metric == 'pcd'):
distance_vector = self.tonic_estimate(
dist,
peak_idxs,
mode_dist,
distance_method=distance_method,
metric=metric)
for r in range(rank):
idx = np.argmin(distance_vector)
tonic_list[r] = mf.cent_to_hz([dist.bins[peak_idxs[idx]]],
anti_freq)[0]
distance_vector[idx] = (np.amax(distance_vector) + 1)
return tonic_list
elif (metric == 'pd'):
distance_vector = self.tonic_estimate(
dist,
shift_idxs,
mode_dist,
distance_method=distance_method,
metric=metric)
for r in range(rank):
idx = np.argmin(distance_vector)
tonic_list[r] = mf.cent_to_hz(
[shift_idxs[idx] * self.cent_ss], ref_freq)[0]
distance_vector[idx] = (np.amax(distance_vector) + 1)
return tonic_list
elif (est_mode):
distance_vector = self.mode_estimate(
dist,
mode_dists,
distance_method=distance_method,
metric=metric)
for r in range(rank):
idx = np.argmin(distance_vector)
mode_list[r] = mode_names[idx]
distance_vector[idx] = (np.amax(distance_vector) + 1)
return mode_list
else:
# Nothing is expected to be estimated
return 0
def estimate(self, pitch_track, mode_names=[], mode_name='', mode_dir='./', est_tonic=True, est_mode=True, rank=1, distance_method="euclidean", metric='pcd', ref_freq=440): """--------------------------------------------------------------------------------------- This is the high level function that users are expected to interact with, for estimation purposes. Using the est_* flags, it is possible to estimate tonic, mode or both. ---------------------------------------------------------------------------------------""" ### Preliminaries before the estimations cent_track = mf.hz_to_cent(pitch_track, ref_freq=ref_freq) dist = mf.generate_pd(cent_track, ref_freq=ref_freq, smooth_factor=self.smooth_factor, cent_ss=self.cent_ss) dist = mf.generate_pcd(dist) if (metric=='pcd') else dist mode_dists = [(p_d.load((m + '_' + metric + '.json'), mode_dir)) for m in mode_names] mode_dist = p_d.load((mode_name + '_' + metric + '.json'), mode_dir) if (mode_name!='') else None tonic_list = np.zeros(rank) mode_list = ['' for x in range(rank)] if(est_tonic): if(metric=='pcd'): ### Shifting to the global minima to prevent wrong detection of peaks shift_factor = dist.vals.tolist().index(min(dist.vals)) dist = dist.shift(shift_factor) anti_freq = mf.cent_to_hz([dist.bins[shift_factor]], ref_freq=ref_freq)[0] peak_idxs, peak_vals = dist.detect_peaks() elif(metric=='pd'): peak_idxs, peak_vals = dist.detect_peaks() origin = np.where(dist.bins==0)[0][0] shift_idxs = [(idx - origin) for idx in peak_idxs] ### Call to actual estimation functions if(est_tonic and est_mode): if(metric=='pcd'): dist_mat = mf.generate_distance_matrix(dist, peak_idxs, mode_dists, method=distance_method) for r in range(rank): min_row = np.where((dist_mat == np.amin(dist_mat)))[0][0] min_col = np.where((dist_mat == np.amin(dist_mat)))[1][0] tonic_list[r] = mf.cent_to_hz([dist.bins[peak_idxs[min_row]]], anti_freq)[0] mode_list[r] = mode_names[min_col] dist_mat[min_row][min_col] = (np.amax(dist_mat) + 1) return mode_list, tonic_list elif(metric=='pd'): dist_mat = np.zeros((len(shift_idxs), len(mode_dists))) for m in range(len(mode_dists)): dist_mat[:,m] = self.tonic_estimate(dist, shift_idxs, mode_dists[m], distance_method=distance_method, metric=metric) for r in range(rank): min_row = np.where((dist_mat == np.amin(dist_mat)))[0][0] min_col = np.where((dist_mat == np.amin(dist_mat)))[1][0] tonic_list[r] = mf.cent_to_hz([shift_idxs[min_row] * self.cent_ss], ref_freq)[0] mode_list[r] = mode_names[min_col] dist_mat[min_row][min_col] = (np.amax(dist_mat) + 1) return mode_list, tonic_list elif(est_tonic): if(metric=='pcd'): distance_vector = self.tonic_estimate(dist, peak_idxs, mode_dist, distance_method=distance_method, metric=metric) for r in range(rank): idx = np.argmin(distance_vector) tonic_list[r] = mf.cent_to_hz([dist.bins[peak_idxs[idx]]], anti_freq)[0] distance_vector[idx] = (np.amax(distance_vector) + 1) return tonic_list elif(metric=='pd'): distance_vector = self.tonic_estimate(dist, shift_idxs, mode_dist, distance_method=distance_method, metric=metric) for r in range(rank): idx = np.argmin(distance_vector) tonic_list[r] = mf.cent_to_hz([shift_idxs[idx] * self.cent_ss], ref_freq)[0] distance_vector[idx] = (np.amax(distance_vector) + 1) return tonic_list elif(est_mode): distance_vector = self.mode_estimate(dist, mode_dists, distance_method=distance_method, metric=metric) for r in range(rank): idx = np.argmin(distance_vector) mode_list[r] = mode_names[idx] distance_vector[idx] = (np.amax(distance_vector) + 1) return mode_list else: # Nothing is expected to be estimated return 0
