source: python/aubio/onsetcompare.py @ a0fd4e4

feature/autosinkfeature/cnnfeature/cnn_orgfeature/constantqfeature/crepefeature/crepe_orgfeature/pitchshiftfeature/pydocstringsfeature/timestretchfix/ffmpeg5pitchshiftsamplertimestretchyinfft+
Last change on this file since a0fd4e4 was 7445aea, checked in by Paul Brossier <piem@altern.org>, 20 years ago

updated aubiocompare-onset and onsetcompare.py

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File size: 3.9 KB
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1"""Copyright (C) 2004 Paul Brossier <piem@altern.org>
2print aubio.__LICENSE__ for the terms of use
3"""
4
5__LICENSE__ = """\
6     Copyright (C) 2004 Paul Brossier <piem@altern.org>
7
8     This program is free software; you can redistribute it and/or modify
9     it under the terms of the GNU General Public License as published by
10     the Free Software Foundation; either version 2 of the License, or
11     (at your option) any later version.
12
13     This program is distributed in the hope that it will be useful,
14     but WITHOUT ANY WARRANTY; without even the implied warranty of
15     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16     GNU General Public License for more details.
17
18     You should have received a copy of the GNU General Public License
19     along with this program; if not, write to the Free Software
20     Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
21"""           
22
23""" this file contains routines to compare two lists of onsets or notes.
24it somewhat implements the Receiver Operating Statistic (ROC).
25see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
26"""
27
28from numarray import *
29
30def onset_roc(la, lb, eps):
31    """ thanks to nicolas wack for the rewrite"""
32    """ compute differences between two lists """
33    """ feature: scalable to huge lists """
34    n, m = len(la), len(lb)
35    if m == 0 :
36        return 0,0,0,n,0
37    missed, bad = 0, 0
38    # find missed ones first
39    for x in la:
40        correspond = 0
41        for y in lb:
42            if abs(x-y) <= eps:
43                correspond += 1
44        if correspond == 0:
45            missed += 1
46    # then look for bad ones
47    for y in lb:
48        correspond = 0
49        for x in la:
50            if abs(x-y) <= eps:
51               correspond += 1
52        if correspond == 0:
53            bad += 1
54    ok    = n - missed
55    hits  = m - bad
56    # at this point, we must have ok = hits. if not we had
57    #   - a case were one onset counted for two labels (ok>hits)
58    #   - a case were one labels matched two onsets (hits>ok)
59    # bad hack for now (fails if both above cases have happened):
60    if ok > hits: bad += ok-hits; ok = hits
61    if hits > ok: missed += hits-ok; hits = ok
62    total = n
63    return ok,bad,missed,total,hits
64   
65   
66def notes_roc (la, lb, eps):
67    """ creates a matrix of size len(la)*len(lb) then look for hit and miss
68    in it within eps tolerance windows """
69    gdn,fpw,fpg,fpa,fdo,fdp = 0,0,0,0,0,0
70    m = len(la)
71    n = len(lb)
72    x =           resize(la[:,0],(n,m))
73    y = transpose(resize(lb[:,0],(m,n)))
74    teps =  (abs(x-y) <= eps[0]) 
75    x =           resize(la[:,1],(n,m))
76    y = transpose(resize(lb[:,1],(m,n)))
77    tpitc = (abs(x-y) <= eps[1]) 
78    res = teps * tpitc
79    res = add.reduce(res,axis=0)
80    for i in range(len(res)) :
81        if res[i] > 1:
82            gdn+=1
83            fdo+=res[i]-1
84        elif res [i] == 1:
85            gdn+=1
86    fpa = n - gdn - fpa
87    return gdn,fpw,fpg,fpa,fdo,fdp
88
89def load_onsets(filename) :
90    """ load onsets targets / candidates files in arrays """
91    l = [];
92   
93    f = open(filename,'ro')
94    while 1:
95        line = f.readline().split()
96        if not line : break
97        l.append(float(line[0]))
98   
99    return l
100
101"""
102def onset_roc (la, lb, eps):
103    \"\"\" build a matrix of all possible differences between two lists \"\"\"
104    \"\"\" bug: not scalable to huge lists \"\"\"
105        n, m        = len(la), len(lb)
106    if m ==0 :
107        return 0,0,0,n,0
108        missed, bad = 0, 0
109        x           = resize(la[:],(m,n))
110        y           = transpose(resize(lb[:],(n,m)))
111        teps        = (abs(x-y) <= eps)
112        resmis      = add.reduce(teps,axis = 0)
113        for i in range(n) :
114        if resmis[i] == 0:
115            missed += 1
116    resbad = add.reduce(teps,axis=1)
117    for i in range(m) :
118        if resbad[i] == 0:
119            bad += 1
120    ok    = n - missed
121    hits  = m - bad
122    total = n
123    return ok,bad,missed,total,hits
124"""
125
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