In [9]:

    

import glob
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import scipy
import shutil
import seaborn as sns

from statsmodels.stats.multicomp import (pairwise_tukeyhsd,
                                         MultiComparison)

import msaf

# Plotting settings
%matplotlib inline
sns.set_style("dark")

ds_base = "/home/uri/datasets/"
dataset_path = "/home/uri/datasets/BeatlesTUT/"
# dataset_path = "/Users/uriadmin/datasets/BeatlesTUT/"
n_jobs = 8
n_octaves = [4, 5, 6, 7]
f_mins = [27.5 * 2 ** (i / 12.) for i in xrange(0, 12, 2)]
labels_ids = ["scluster", "siplca", "fmc2d", "cnmf", "cc"]
bounds_ids = ["sf", "cnmf", "foote", "cc", "olda", "scluster", "siplca"]
features = ["hpcp", "mfcc", "tonnetz", "cqt"]
n_mffc_coeffs = range(7, 20)


    

In [ ]:

    

# Boundaries for MFCC
feature = "mfcc"
for n_mfcc_coeff in n_mffc_coeffs:
    print "MFCC Coeffs: ", n_mfcc_coeff
    msaf.Anal.mfcc_coeff = n_mfcc_coeff
    msaf.featextract.process(dataset_path, n_jobs=n_jobs, overwrite=True)
    for bound_id in bounds_ids:
        print "\t bounds_id:", bound_id
        results = msaf.process(dataset_path, feature=feature, boundaries_id=bound_id, n_jobs=n_jobs)
        results = msaf.eval.process(dataset_path, bound_id, feature=feature, save=True, n_jobs=n_jobs)
    key = "mfcc_coeffE%d" % n_mfcc_coeff
    shutil.move(os.path.join(dataset_path, "features"), os.path.join(dataset_path, "features_%s" % key))
    shutil.move("results", "results_%s" % key)


    

In [12]:

    

# Labels for MFCC (Assuming features have already been computed)
for n_mfcc_coeff in n_mffc_coeffs:
    print "MFCC Coeffs: ", n_mfcc_coeff
    msaf.Anal.mfcc_coeff = n_mfcc_coeff
    key = "mfcc_coeffE%d" % n_mfcc_coeff
    shutil.move(os.path.join(dataset_path, "features_%s" % key), os.path.join(dataset_path, "features"))
    for label_id in labels_ids:
        print "\t bounds_id:", label_id
        results = msaf.process(dataset_path, feature=feature, labels_id=label_id, n_jobs=n_jobs)
        results = msaf.eval.process(dataset_path, "gt", labels_id=label_id, feature=feature, save=True, n_jobs=n_jobs)
    shutil.move(os.path.join(dataset_path, "features"), os.path.join(dataset_path, "features_%s" % key))
    shutil.move("results", "results_%s" % key)


    

    




MFCC Coeffs:  7
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  8
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  9
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  10
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  11
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  12
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  13
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  14
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  15
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  16
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  17
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  18
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc
MFCC Coeffs:  19
	 bounds_id: fmc2d
	 bounds_id: cnmf
	 bounds_id: cc

In [ ]:

    

# Boundaries
for n_octave in n_octaves:
    for f_min in f_mins:
        msaf.Anal.f_min = f_min
        msaf.Anal.n_octaves = n_octave
        msaf.featextract.process(dataset_path, n_jobs=n_jobs, overwrite=True)
        for bound_id in bounds_ids:
            results = msaf.process(dataset_path, feature="hpcp", boundaries_id=bound_id, n_jobs=n_jobs)
            results = msaf.eval.process(dataset_path, bound_id, save=True, n_jobs=n_jobs)
        key = "noctavesE%d_fminE%.1f" % (n_octave, f_min)
        shutil.move(os.path.join(dataset_path, "features"), os.path.join(dataset_path, "features_%s" % key))
        shutil.move("results", "results_%s" % key)


    

In [4]:

    

# Labels (Assuming features have already been computed)
for n_octave in n_octaves:
    for f_min in f_mins:
        if n_octave == 7 and f_min > 40:
            continue
        msaf.Anal.f_min = f_min
        msaf.Anal.n_octaves = n_octave
        key = "noctavesE%d_fminE%.1f" % (n_octave, f_min)
        shutil.move(os.path.join(dataset_path, "features_%s" % key), os.path.join(dataset_path, "features"))
        for label_id in labels_ids:
            results = msaf.process(dataset_path, feature="hpcp", labels_id=label_id, n_jobs=n_jobs)
            results = msaf.eval.process(dataset_path, "gt", labels_id=label_id, save=True, n_jobs=n_jobs)
        shutil.move(os.path.join(dataset_path, "features"), os.path.join(dataset_path, "features_%s" % key))
        shutil.move("results", "results_%s" % key)


    

In [2]:

    

# Explore different features
# Run boundaries with different features
for bounds_id in bounds_ids:
    print "bounds", bounds_id
    if bounds_id != "siplca":
        continue# Labels (Assuming features have already been computed)
for n_octave in n_octaves:
    for f_min in f_mins:
        if n_octave == 7 and f_min > 40:
            continue
        msaf.Anal.f_min = f_min
        msaf.Anal.n_octaves = n_octave
        key = "noctavesE%d_fminE%.1f" % (n_octave, f_min)
        shutil.move(os.path.join(dataset_path, "features_%s" % key), os.path.join(dataset_path, "features"))
        for label_id in labels_ids:
            results = msaf.process(dataset_path, feature="hpcp", labels_id=label_id, n_jobs=n_jobs)
            results = msaf.eval.process(dataset_path, "gt", labels_id=label_id, save=True, n_jobs=n_jobs)
        shutil.move(os.path.join(dataset_path, "features"), os.path.join(dataset_path, "features_%s" % key))
        shutil.move("results", "results_%s" % key)
    for feature in features:
        results = msaf.process(dataset_path, feature=feature, boundaries_id=bounds_id, n_jobs=n_jobs)
        results = msaf.eval.process(dataset_path, bounds_id, save=True, n_jobs=n_jobs, feature=feature)


    

    




bounds cc
bounds olda
bounds scluster
bounds siplca

In [3]:

    

# Run labels with different features
for labels_id in labels_ids:
    for feature in features:
        try:
            results = msaf.process(dataset_path, feature=feature, labels_id=labels_id, n_jobs=n_jobs)
            results = msaf.eval.process(dataset_path, "gt", labels_id=labels_id, save=True, n_jobs=n_jobs, feature=feature)
        except RuntimeError as e:
            print "Warning: ", e


    

In [16]:

    

# Explore different datasets
dataset_names = ["Cerulean", "Epiphyte", "Isophonics", "SALAMI"]
feature = "hpcp"
dataset_path = "/home/uri/datasets/Segments/"


    

In [ ]:

    

# Run boundaries with different datasets
for ds_name in dataset_names:
    print "Computing boundaries for %s" % ds_name
    for bounds_id in bounds_ids:
        results = msaf.process(dataset_path, feature=feature, boundaries_id=bounds_id, 
                               n_jobs=n_jobs, ds_name=ds_name)
        results = msaf.eval.process(dataset_path, bounds_id, save=True, n_jobs=n_jobs, 
                                    feature=feature, ds_name=ds_name)


    

In [ ]:

    

# Run labels with different datasets
for ds_name in dataset_names:
    print "Computing labels for %s" % ds_name
    for labels_id in labels_ids:
        try:
            results = msaf.process(dataset_path, feature=feature, labels_id=labels_id, 
                                   n_jobs=n_jobs, ds_name=ds_name)
            results = msaf.eval.process(dataset_path, "gt", labels_id=labels_id, save=True, n_jobs=n_jobs, 
                                        feature=feature, ds_name=ds_name)
        except RuntimeError as e:
            print "Warning: ", e


    

In [7]:

    

# Do the same for the Beatles-TUT / SPAM dataset
dataset_path = "/home/uri/datasets/BeatlesTUT/"
dataset_path = "/home/uri/datasets/Sargon/"

# Run boundaries for the Beatles-TUT
print "Computing boundaries for Beatles-TUT"
for bounds_id in bounds_ids:
    results = msaf.process(dataset_path, feature=feature, boundaries_id=bounds_id, 
                           n_jobs=n_jobs)
    results = msaf.eval.process(dataset_path, bounds_id, save=True, n_jobs=n_jobs, 
                                feature=feature)

# Run labels for the Beatles-TUT
print "Computing labels for Beatles-TUT"
for labels_id in labels_ids:
    try:
        results = msaf.process(dataset_path, feature=feature, labels_id=labels_id, 
                               n_jobs=n_jobs)
        results = msaf.eval.process(dataset_path, "gt", labels_id=labels_id, save=True, n_jobs=n_jobs, 
                                    feature=feature)
    except RuntimeError as e:
        print "Warning: ", e


    

    




Computing boundaries for Beatles-TUT
Computing labels for Beatles-TUT

In [8]:

    

# Explore different datasets
ds_name = "*"
feature = "hpcp"
dataset_path = "/home/uri/datasets/SPAM/"
annotators = np.arange(5)


    

In [3]:

    

# Run boundaries for all the annotators in Cerulean
for annotator_id in annotators:
    for bounds_id in bounds_ids:
        if bounds_id != "cc":
            continue
        print "Bounds", bounds_id, "Annotator", annotator_id
        results = msaf.process(dataset_path, feature=feature, boundaries_id=bounds_id, 
                               n_jobs=n_jobs, annotator_id=annotator_id, ds_name=ds_name)
        results = msaf.eval.process(dataset_path, bounds_id, save=True, n_jobs=n_jobs, 
                                    feature=feature, annotator_id=annotator_id, ds_name=ds_name)

# Run labels for all the annotators in SubSegments
print "Computing labels for multiple annotators"
for annotator_id in annotators:
    for labels_id in labels_ids:
        if labels_id != "cc":
            continue
        try:
            results = msaf.process(dataset_path, feature=feature, labels_id=labels_id, 
                                   n_jobs=n_jobs, annotator_id=annotator_id, ds_name=ds_name)
            results = msaf.eval.process(dataset_path, "gt", labels_id=labels_id, save=True, n_jobs=n_jobs, 
                                        feature=feature, annotator_id=annotator_id, ds_name=ds_name)
        except RuntimeError as e:
            print "Warning: ", e


    

    




Bounds cc Annotator 0
Bounds cc Annotator 1
Bounds cc Annotator 2






    




WARNING:root:Couldn't compute the Information Gain for file /home/uri/datasets/SPAM/estimations/SPAM_SALAMI_78.jams






    




Bounds cc Annotator 3
Bounds cc Annotator 4
Computing labels for multiple annotators






    




WARNING:root:Couldn't compute the Information Gain for file /home/uri/datasets/SPAM/estimations/SPAM_Cerulean_Boston_Symphony_Orchestra_&_Charles_Munch-Sympho.jams
WARNING:root:Couldn't compute the Information Gain for file /home/uri/datasets/SPAM/estimations/SPAM_SALAMI_78.jams
WARNING:root:Couldn't compute the Information Gain for file /home/uri/datasets/SPAM/estimations/SPAM_Cerulean_Boston_Symphony_Orchestra_&_Charles_Munch-Sympho.jams

In [2]:

    

# Explore different datasets
feature = "hpcp"
dataset_path = "/home/uri/datasets/BeatlesTUT/"
bounds_ids += ["gt"]
annotator_id = 0
ds_name="*"
print bounds_ids


    

    




['sf', 'cnmf', 'foote', 'cc', 'olda', 'scluster', 'siplca', 'gt']

In [3]:

    

for bounds_id in bounds_ids:
    for labels_id in labels_ids:
            print "Computing: ", bounds_id, labels_id
            results = msaf.process(dataset_path, feature=feature, boundaries_id=bounds_id, labels_id=labels_id, 
                                   n_jobs=n_jobs, annotator_id=annotator_id, ds_name=ds_name)
            results = msaf.eval.process(dataset_path, bounds_id, labels_id=labels_id, save=True, n_jobs=n_jobs, 
                                        feature=feature, annotator_id=annotator_id, ds_name=ds_name)


    

    




Computing:  sf scluster
Computing:  sf siplca
Computing:  sf fmc2d
Computing:  sf cnmf
Computing:  sf cc
Computing:  cnmf scluster
Computing:  cnmf siplca
Computing:  cnmf fmc2d
Computing:  cnmf cnmf
Computing:  cnmf cc
Computing:  foote scluster
Computing:  foote siplca
Computing:  foote fmc2d
Computing:  foote cnmf
Computing:  foote cc
Computing:  cc scluster
Computing:  cc siplca
Computing:  cc fmc2d
Computing:  cc cnmf
Computing:  cc cc
Computing:  olda scluster
Computing:  olda siplca
Computing:  olda fmc2d
Computing:  olda cnmf
Computing:  olda cc
Computing:  scluster scluster
Computing:  scluster siplca
Computing:  scluster fmc2d
Computing:  scluster cnmf
Computing:  scluster cc
Computing:  siplca scluster
Computing:  siplca siplca
Computing:  siplca fmc2d
Computing:  siplca cnmf
Computing:  siplca cc
Computing:  gt scluster
Computing:  gt siplca
Computing:  gt fmc2d
Computing:  gt cnmf
Computing:  gt cc

In [ ]: