Mercurial > hg > emotion-detection-top-level
comparison Code/Classifiers/kmeans_Formants_Singing.m @ 4:92ca03a8fa99 tip
Update to ICASSP 2013 benchmark
author | Dawn Black |
---|---|
date | Wed, 13 Feb 2013 11:02:39 +0000 |
parents | |
children |
comparison
equal
deleted
inserted
replaced
3:e1cfa7765647 | 4:92ca03a8fa99 |
---|---|
1 function [] = kmeans_Formants_Singing( varargin ) | |
2 | |
3 cd 'C:\Users\dawn\Dropbox\TestResults' | |
4 | |
5 DEBUG = 0; | |
6 % output results file name | |
7 masterFileOutputID = fopen( 'kmeans_paper_SingingFormantsStats.txt', 'a' ); | |
8 % input results file name | |
9 % inputFileName = 'paper_singingFormantsStats_ZhangShuo.txt'; | |
10 % inputFileName = 'paper_singingFormantsStats_WangXinnong.txt'; | |
11 inputFileName = 'paper_singingFormantsStats.txt'; | |
12 | |
13 % This function allows the user to stipulate which Singing voice LLD's they | |
14 % wish to forward to a k-means classifier and produces a file of | |
15 % performance characteristics. Input arguments stipulate the LLD's and | |
16 % there is a choice of:- | |
17 % | |
18 % ---- PRAAT FORMANT MEASUREMENTS ---- | |
19 % '_Formant_Burg' | |
20 % '_Formant_all' | |
21 % '_Formant_robust' | |
22 % | |
23 % A text file entitled kmeans_Singing_LLD1name_LLD2name_ ... LLDNname.txt | |
24 % is produced that contains the results of the k-mean classification for | |
25 % the LLD's specified and named in the result document title. | |
26 | |
27 fprintf( masterFileOutputID, '\n RESULTS FILE NAME: %s\n', inputFileName); | |
28 inputFileID = fopen( inputFileName ); | |
29 | |
30 % get the column numbers of the results that we want to classify | |
31 | |
32 % COLUMN NUMBER : METRIC | |
33 % | |
34 % ------------- BURG FORMANTS --------------- | |
35 % 11 : Number of BURG formants listed = nBF | |
36 % | |
37 % THERE ARE CURRENTLY 24 MEASUREMENTS TAKEN FOR EACH FORMANT | |
38 nMetrics = 24; | |
39 % | |
40 % 12 : mean frequency of the first BURG formant | |
41 % 13 : variance of the first BURG formant | |
42 % 14 : minimum frequency of the first BURG formant | |
43 % 15 : maximum frequency of the first BURG formant | |
44 % 16 : mean Frequency Derivative of the first BURG formant | |
45 % 17 : varience of the Frequency Derivative of the first BURG formant | |
46 % 18 : min of the Frequency Derivative of the first BURG formant | |
47 % 19 : max of the Frequency Derivative of the first BURG formant | |
48 % 20 : mean of the Frequency 2nd Derivative of the first BURG formant | |
49 % 21 : varience of the Frequency 2nd Derivative of the first BURG formant | |
50 % 22 : min of the Frequency 2nd Derivative of the first BURG formant | |
51 % 23 : max of the Frequency 2nd Derivative of the first BURG formant | |
52 % 24 : mean of the Bandwidth of the first BURG formant | |
53 % 25 : varience of the Bandwidth of the first BURG formant | |
54 % 26 : min of the Bandwidth of the first BURG formant | |
55 % 27 : max of the Bandwidth of the first BURG formant | |
56 % 28 : mean of the Bandwidth Derivative of the first BURG formant | |
57 % 29 : varience of the Bandwidth Derivative of the first BURG formant | |
58 % 30 : min of the Bandwidth Derivative of the first BURG formant | |
59 % 31 : max of the Bandwidth Derivative of the first BURG formant | |
60 % 32 : mean of the Bandwidth 2nd Derivative of the first BURG formant | |
61 % 33 : var of the Bandwidth 2nd Derivative of the first BURG formant | |
62 % 34 : min of the Bandwidth 2nd Derivative of the first BURG formant | |
63 % 35 : max of the Bandwidth 2nd Derivative of the first BURG formant | |
64 % | |
65 % ....... there are nMetrics for each formant in nBF formants, so cycle | |
66 % through until the last is reached ...... | |
67 % | |
68 % 36 + ((nBF-1)*nMetrics) : mean frequency of the nBF BURG formant | |
69 % 36 + ((nBF-1)*nMetrics) + 1 : variance of the nBF BURG formant | |
70 % 36 + ((nBF-1)*nMetrics) + 2 : minimum frequency of the nBF BURG formant | |
71 % 36 + ((nBF-1)*nMetrics) + 3 : maximum frequency of the nBF BURG formant | |
72 % 36 + ((nBF-1)*nMetrics) + 4 : mean Frequency Derivative of the nBF BURG formant | |
73 % 36 + ((nBF-1)*nMetrics) + 5 : varience of the Frequency Derivative of the nBF BURG formant | |
74 % 36 + ((nBF-1)*nMetrics) + 6 : min of the Frequency Derivative of the nBF BURG formant | |
75 % 36 + ((nBF-1)*nMetrics) + 7 : max of the Frequency Derivative of the nBF BURG formant | |
76 % 36 + ((nBF-1)*nMetrics) + 8 : mean of the Frequency 2nd Derivative of the nBF BURG formant | |
77 % 36 + ((nBF-1)*nMetrics) + 9 : varience of the Frequency 2nd Derivative of the nBF BURG formant | |
78 % 36 + ((nBF-1)*nMetrics) + 10 : min of the Frequency 2nd Derivative of the nBF BURG formant | |
79 % 36 + ((nBF-1)*nMetrics) + 11 : max of the Frequency 2nd Derivative of the nBF BURG formant | |
80 % 36 + ((nBF-1)*nMetrics) + 12 : mean of the Bandwidth of the nBF BURG formant | |
81 % 36 + ((nBF-1)*nMetrics) + 13 : varience of the Bandwidth of the nBF BURG formant | |
82 % 36 + ((nBF-1)*nMetrics) + 14 : min of the Bandwidth of the nBF BURG formant | |
83 % 36 + ((nBF-1)*nMetrics) + 15 : max of the Bandwidth of the nBF BURG formant | |
84 % 36 + ((nBF-1)*nMetrics) + 16 : mean of the Bandwidth Derivative of the nBF BURG formant | |
85 % 36 + ((nBF-1)*nMetrics) + 17 : variece of the Bandwidth Derivative of the nBF BURG formant | |
86 % 36 + ((nBF-1)*nMetrics) + 18 : min of the Bandwidth Derivative of the nBF BURG formant | |
87 % 36 + ((nBF-1)*nMetrics) + 19 : max of the Bandwidth Derivative of the nBF BURG formant | |
88 % 36 + ((nBF-1)*nMetrics) + 20 : mean of the Bandwidth 2nd Derivative of the nBF BURG formant | |
89 % 36 + ((nBF-1)*nMetrics) + 21 : var of the Bandwidth 2nd Derivative of the nBF BURG formant | |
90 % 36 + ((nBF-1)*nMetrics) + 22 : min of the Bandwidth 2nd Derivative of the nBF BURG formant | |
91 % 36 + ((nBF-1)*nMetrics) + 23 : max of the Bandwidth 2nd Derivative of the nBF BURG formant | |
92 % | |
93 % FOR THE MEAN OF ALL BURG FORMANTS | |
94 % 36 + (nBF*nMetrics) : mean of all formants Frequency | |
95 % 36 + (nBF*nMetrics) + 1 : varience of the mean of all formants Frequency | |
96 % 36 + (nBF*nMetrics) + 2 : minimum of the mean of all formants Frequency | |
97 % 36 + (nBF*nMetrics) + 3 : maximum of the mean of all formants Frequency | |
98 % 36 + (nBF*nMetrics) + 4 : mean of all formants mean Frequency Derivative | |
99 % 36 + (nBF*nMetrics) + 5 : mean of all formants varience Frequency Derivative | |
100 % 36 + (nBF*nMetrics) + 6 : min of the mean of all formants Frequency Derivative | |
101 % 36 + (nBF*nMetrics) + 7 : max of the mean of all formants Frequency Derivative | |
102 % 36 + (nBF*nMetrics) + 8 : mean of the mean of all formants Frequency 2nd Derivative | |
103 % 36 + (nBF*nMetrics) + 9 : varience of the mean of all formants Frequency 2nd Derivative | |
104 % 36 + (nBF*nMetrics) + 10 : min of the mean of all formants Frequency 2nd Derivative | |
105 % 36 + (nBF*nMetrics) + 11 : max of the mean of all formants Frequency 2nd Derivative | |
106 % | |
107 % ------------- ALL FORMANTS --------------- | |
108 % | |
109 % 36 + (nBF*nMetrics) + 12 : Number of ALL formants listed = nAF | |
110 % | |
111 % startOfALLMeasurements = 36 + (nBF*nMetrics) + 13; | |
112 % | |
113 % startOfALLMeasurements : mean frequency of the first ALL formant | |
114 % startOfALLMeasurements + 1 : variance of the first ALL formant | |
115 % startOfALLMeasurements + 2 : minimum frequency of the first ALL formant | |
116 % startOfALLMeasurements + 3 : maximum frequency of the first ALL formant | |
117 % startOfALLMeasurements + 4 : mean Frequency Derivative of the first ALL formant | |
118 % startOfALLMeasurements + 5 : varience of the Frequency Derivative of the first ALL formant | |
119 % startOfALLMeasurements + 6 : min of the Frequency Derivative of the first ALL formant | |
120 % startOfALLMeasurements + 7 : max of the Frequency Derivative of the first ALL formant | |
121 % startOfALLMeasurements + 8 : mean of the Frequency 2nd Derivative of the first ALL formant | |
122 % startOfALLMeasurements + 9 : varience of the Frequency 2nd Derivative of the first ALL formant | |
123 % startOfALLMeasurements + 10 : min of the Frequency 2nd Derivative of the first ALL formant | |
124 % startOfALLMeasurements + 11 : max of the Frequency 2nd Derivative of the first ALL formant | |
125 % startOfALLMeasurements + 12 : mean of the Bandwidth of the first ALL formant | |
126 % startOfALLMeasurements + 13 : varience of the Bandwidth of the first ALL formant | |
127 % startOfALLMeasurements + 14 : min of the Bandwidth of the first ALL formant | |
128 % startOfALLMeasurements + 15 : max of the Bandwidth of the first ALL formant | |
129 % startOfALLMeasurements + 16 : mean of the Bandwidth Derivative of the first ALL formant | |
130 % startOfALLMeasurements + 17 : varience of the Bandwidth Derivative of the first ALL formant | |
131 % startOfALLMeasurements + 18 : min of the Bandwidth Derivative of the first ALL formant | |
132 % startOfALLMeasurements + 19 : max of the Bandwidth Derivative of the first ALL formant | |
133 % startOfALLMeasurements + 20 : mean of the Bandwidth 2nd Derivative of the first ALL formant | |
134 % startOfALLMeasurements + 21 : var of the Bandwidth 2nd Derivative of the first ALL formant | |
135 % startOfALLMeasurements + 22 : min of the Bandwidth 2nd Derivative of the first ALL formant | |
136 % startOfALLMeasurements + 23 : max of the Bandwidth 2nd Derivative of the first ALL formant | |
137 % | |
138 % ....... there are nMetrics for each formant in nAF formants, so cycle | |
139 % through until the last is reached ...... | |
140 % | |
141 % startOfALLMeasurements + ((nAF-1)*nMetrics) : mean frequency of the nAF ALL formant | |
142 % startOfALLMeasurements + ((nAF-1)*nMetrics) : variance of the nAF ALL formant | |
143 % startOfALLMeasurements + ((nAF-1)*nMetrics) : minimum frequency of the nAF ALL formant | |
144 % startOfALLMeasurements + ((nAF-1)*nMetrics) : maximum frequency of the nAF ALL formant | |
145 % startOfALLMeasurements + ((nAF-1)*nMetrics) : mean Frequency Derivative of the nAF ALL formant | |
146 % startOfALLMeasurements + ((nAF-1)*nMetrics) : varience of the Frequency Derivative of the nAF ALL formant | |
147 % startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Frequency Derivative of the nAF ALL formant | |
148 % startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Frequency Derivative of the nAF ALL formant | |
149 % startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Frequency 2nd Derivative of the nAF ALL formant | |
150 % startOfALLMeasurements + ((nAF-1)*nMetrics) : varience of the Frequency 2nd Derivative of the nAF ALL formant | |
151 % startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Frequency 2nd Derivative of the nAF ALL formant | |
152 % startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Frequency 2nd Derivative of the nAF ALL formant | |
153 % startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Bandwidth of the nAF ALL formant | |
154 % startOfALLMeasurements + ((nAF-1)*nMetrics) : varience of the Bandwidth of the nAF ALL formant | |
155 % startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Bandwidth of the nAF ALL formant | |
156 % startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Bandwidth of the nAF ALL formant | |
157 % startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Bandwidth Derivative of the nAF ALL formant | |
158 % startOfALLMeasurements + ((nAF-1)*nMetrics) : variece of the Bandwidth Derivative of the nAF ALL formant | |
159 % startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Bandwidth Derivative of the nAF ALL formant | |
160 % startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Bandwidth Derivative of the nAF ALL formant | |
161 % startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Bandwidth 2nd Derivative of the nAF ALL formant | |
162 % startOfALLMeasurements + ((nAF-1)*nMetrics) : var of the Bandwidth 2nd Derivative of the nAF ALL formant | |
163 % startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Bandwidth 2nd Derivative of the nAF ALL formant | |
164 % startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Bandwidth 2nd Derivative of the nAF ALL formant | |
165 % | |
166 % FOR THE MEAN OF ALL ALL FORMANTS | |
167 % startOfALLMeasurements + (nAF*nMetrics) : mean of all formants Frequency | |
168 % startOfALLMeasurements + (nAF*nMetrics) + 1 : varience of the mean of all formants Frequency | |
169 % startOfALLMeasurements + (nAF*nMetrics) + 2 : minimum of the mean of all formants Frequency | |
170 % startOfALLMeasurements + (nAF*nMetrics) + 3 : maximum of the mean of all formants Frequency | |
171 % startOfALLMeasurements + (nAF*nMetrics) + 4 : mean of all formants mean Frequency Derivative | |
172 % startOfALLMeasurements + (nAF*nMetrics) + 5 : mean of all formants varience Frequency Derivative | |
173 % startOfALLMeasurements + (nAF*nMetrics) + 6 : min of the mean of all formants Frequency Derivative | |
174 % startOfALLMeasurements + (nAF*nMetrics) + 7 : max of the mean of all formants Frequency Derivative | |
175 % startOfALLMeasurements + (nAF*nMetrics) + 8 : mean of the mean of all formants Frequency 2nd Derivative | |
176 % startOfALLMeasurements + (nAF*nMetrics) + 9 : varience of the mean of all formants Frequency 2nd Derivative | |
177 % startOfALLMeasurements + (nAF*nMetrics) + 10 : min of the mean of all formants Frequency 2nd Derivative | |
178 % startOfALLMeasurements + (nAF*nMetrics) + 11 : max of the mean of all formants Frequency 2nd Derivative | |
179 % | |
180 % ------------- ROBUST FORMANTS --------------- | |
181 % | |
182 % startOfALLMeasurements + (nAF*nMetrics) + 12 : Number of ROBUST formants listed = nRF | |
183 % | |
184 % startOfROBUSTMeasurements = startOfALLMeasurements + (nAF*nMetrics) + 13; | |
185 % | |
186 % startOfROBUSTMeasurements : mean frequency of the first ROBUST formant | |
187 % startOfROBUSTMeasurements + 1 : variance of the first ROBUST formant | |
188 % startOfROBUSTMeasurements + 2 : minimum frequency of the first ROBUST formant | |
189 % startOfROBUSTMeasurements + 3 : maximum frequency of the first ROBUST formant | |
190 % startOfROBUSTMeasurements + 4 : mean Frequency Derivative of the first ROBUST formant | |
191 % startOfROBUSTMeasurements + 5 : varience of the Frequency Derivative of the first ROBUST formant | |
192 % startOfROBUSTMeasurements + 6 : min of the Frequency Derivative of the first ROBUST formant | |
193 % startOfROBUSTMeasurements + 7 : max of the Frequency Derivative of the first ROBUST formant | |
194 % startOfROBUSTMeasurements + 8 : mean of the Frequency 2nd Derivative of the first ROBUST formant | |
195 % startOfROBUSTMeasurements + 9 : varience of the Frequency 2nd Derivative of the first ROBUST formant | |
196 % startOfROBUSTMeasurements + 10 : min of the Frequency 2nd Derivative of the first ROBUST formant | |
197 % startOfROBUSTMeasurements + 11 : max of the Frequency 2nd Derivative of the first ROBUST formant | |
198 % startOfROBUSTMeasurements + 12 : mean of the Bandwidth of the first ROBUST formant | |
199 % startOfROBUSTMeasurements + 13 : varience of the Bandwidth of the first ROBUST formant | |
200 % startOfROBUSTMeasurements + 14 : min of the Bandwidth of the first ROBUST formant | |
201 % startOfROBUSTMeasurements + 15 : max of the Bandwidth of the first ROBUST formant | |
202 % startOfROBUSTMeasurements + 16 : mean of the Bandwidth Derivative of the first ROBUST formant | |
203 % startOfROBUSTMeasurements + 17 : varience of the Bandwidth Derivative of the first ROBUST formant | |
204 % startOfROBUSTMeasurements + 18 : min of the Bandwidth Derivative of the first ROBUST formant | |
205 % startOfROBUSTMeasurements + 19 : max of the Bandwidth Derivative of the first ROBUST formant | |
206 % startOfROBUSTMeasurements + 20 : mean of the Bandwidth 2nd Derivative of the first ROBUST formant | |
207 % startOfROBUSTMeasurements + 21 : var of the Bandwidth 2nd Derivative of the first ROBUST formant | |
208 % startOfROBUSTMeasurements + 22 : min of the Bandwidth 2nd Derivative of the first ROBUST formant | |
209 % startOfROBUSTMeasurements + 23 : max of the Bandwidth 2nd Derivative of the first ROBUST formant | |
210 % | |
211 % ....... there are nMetrics for each formant in nRF formants, so cycle | |
212 % through until the last is reached ...... | |
213 % | |
214 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean frequency of the nRF ROBUST formant | |
215 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : variance of the nRF ROBUST formant | |
216 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : minimum frequency of the nRF ROBUST formant | |
217 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : maximum frequency of the nRF ROBUST formant | |
218 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean Frequency Derivative of the nRF ROBUST formant | |
219 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : varience of the Frequency Derivative of the nRF ROBUST formant | |
220 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Frequency Derivative of the nRF ROBUST formant | |
221 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Frequency Derivative of the nRF ROBUST formant | |
222 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Frequency 2nd Derivative of the nRF ROBUST formant | |
223 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : varience of the Frequency 2nd Derivative of the nRF ROBUST formant | |
224 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Frequency 2nd Derivative of the nRF ROBUST formant | |
225 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Frequency 2nd Derivative of the nRF ROBUST formant | |
226 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Bandwidth of the nRF ROBUST formant | |
227 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : varience of the Bandwidth of the nRF ROBUST formant | |
228 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Bandwidth of the nRF ROBUST formant | |
229 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Bandwidth of the nRF ROBUST formant | |
230 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Bandwidth Derivative of the nRF ROBUST formant | |
231 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : variece of the Bandwidth Derivative of the nRF ROBUST formant | |
232 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Bandwidth Derivative of the nRF ROBUST formant | |
233 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Bandwidth Derivative of the nRF ROBUST formant | |
234 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Bandwidth 2nd Derivative of the nRF ROBUST formant | |
235 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : var of the Bandwidth 2nd Derivative of the nRF ROBUST formant | |
236 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Bandwidth 2nd Derivative of the nRF ROBUST formant | |
237 % startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Bandwidth 2nd Derivative of the nRF ROBUST formant | |
238 % | |
239 % FOR THE MEAN OF ALL ROBUST FORMANTS | |
240 % startOfROBUSTMeasurements + (nRF*nMetrics) : mean of all formants Frequency | |
241 % startOfROBUSTMeasurements + (nRF*nMetrics) + 1 : varience of the mean of all formants Frequency | |
242 % startOfROBUSTMeasurements + (nRF*nMetrics) + 2 : minimum of the mean of all formants Frequency | |
243 % startOfROBUSTMeasurements + (nRF*nMetrics) + 3 : maximum of the mean of all formants Frequency | |
244 % startOfROBUSTMeasurements + (nRF*nMetrics) + 4 : mean of all formants mean Frequency Derivative | |
245 % startOfROBUSTMeasurements + (nRF*nMetrics) + 5 : mean of all formants varience Frequency Derivative | |
246 % startOfROBUSTMeasurements + (nRF*nMetrics) + 6 : min of the mean of all formants Frequency Derivative | |
247 % startOfROBUSTMeasurements + (nRF*nMetrics) + 7 : max of the mean of all formants Frequency Derivative | |
248 % startOfROBUSTMeasurements + (nRF*nMetrics) + 8 : mean of the mean of all formants Frequency 2nd Derivative | |
249 % startOfROBUSTMeasurements + (nRF*nMetrics) + 9 : varience of the mean of all formants Frequency 2nd Derivative | |
250 % startOfROBUSTMeasurements + (nRF*nMetrics) + 10 : min of the mean of all formants Frequency 2nd Derivative | |
251 % startOfROBUSTMeasurements + (nRF*nMetrics) + 11 : max of the mean of all formants Frequency 2nd Derivative | |
252 % | |
253 | |
254 noOfArguments = length(varargin); | |
255 columnIndices = []; | |
256 | |
257 getBURGFormants = 0; | |
258 getAllFormants=0; | |
259 getRobustFormants=0; | |
260 | |
261 for i=1 : noOfArguments | |
262 if( strcmp( varargin{i}, 'formant_Burg' )) | |
263 getBURGFormants = 1; | |
264 elseif( strcmp( varargin{i}, 'formant_all' )) | |
265 getAllFormants=1; | |
266 elseif( strcmp( varargin{i}, 'formant_robust' )) | |
267 getRobustFormants=1; | |
268 end | |
269 end | |
270 | |
271 titleName = ''; | |
272 for i=1 : noOfArguments | |
273 titleName = [ titleName varargin{i} '_']; | |
274 fprintf( masterFileOutputID, '%s_', varargin{i} ); | |
275 end | |
276 | |
277 fprintf( masterFileOutputID, '\t' ); | |
278 | |
279 % -------------------- get the data from the results file --------------- | |
280 lineCount = 0; | |
281 fileCount = 0; | |
282 data = []; | |
283 while( ~(feof(inputFileID)) ) | |
284 | |
285 outputValues = []; | |
286 % sampleEmotion = []; | |
287 % gender = []; | |
288 | |
289 thestr = fgetl(inputFileID); | |
290 if( lineCount > 10 ) % skip the file header | |
291 fileCount = fileCount + 1; | |
292 | |
293 % determine whether we have a positive or negative sample | |
294 sampleEmotion( fileCount ) = 'U'; | |
295 if( ~(isempty(strfind(thestr,'pos')))) | |
296 % sample is positive | |
297 sampleEmotion( fileCount ) = 'P'; | |
298 elseif( ~(isempty(strfind(thestr,'neg')))) | |
299 % sample is negative | |
300 sampleEmotion( fileCount ) = 'N'; | |
301 else | |
302 disp('EEEK!'); | |
303 pause; | |
304 end | |
305 | |
306 % % determine whether we have a male, female or trans sample | |
307 % gender( fileCount ) = '?'; | |
308 % if( ~(isempty(strfind(thestr,'fem')))) | |
309 % % gender is female | |
310 % gender( fileCount ) = 'F'; | |
311 % elseif( ~(isempty(strfind(thestr,'male')))) | |
312 % % gender is male | |
313 % gender( fileCount ) = 'M'; | |
314 % elseif( ~(isempty(strfind(thestr,'trans')))) | |
315 % % gender is trans | |
316 % gender( fileCount ) = 'T'; | |
317 % else | |
318 % disp('EEEK!'); | |
319 % pause; | |
320 % end | |
321 | |
322 %how many values are in the string? | |
323 spaces = strfind( thestr, ' ' ); | |
324 numberstr = thestr( spaces(1) : end ); % chop off the file name | |
325 frmtpos = strfind( numberstr, 'maxNoOfFormants'); % find the position of the label for number of formants | |
326 | |
327 % str1 = numberstr( 1 : frmtpos(1)-1 ); % string contains jitter and shimmer values | |
328 str2 = numberstr( frmtpos(1) : frmtpos(2)-1 ); % string contains all BURG formant information | |
329 str3 = numberstr( frmtpos(2) : frmtpos(3)-1 ); % string contains all ALL formant information | |
330 str4 = numberstr( frmtpos(3) : end ); % string contains all ROBUST formant information | |
331 | |
332 | |
333 % vars = sscanf( str1, '%f', inf ); | |
334 % % extract the shimmer and jitter values | |
335 % outputValues = [ outputValues vars( columnIndices )']; | |
336 | |
337 if( getBURGFormants ) | |
338 spaces = strfind( str2, ' ' ); % remove the string 'maxNoOfFormants' | |
339 vars = sscanf( str2( spaces(1) : end ), '%f', inf ); | |
340 outputValues = stripOutFormantValues( vars, outputValues ); | |
341 end | |
342 | |
343 if( getAllFormants ) | |
344 spaces = strfind( str3, ' ' ); % remove the string 'maxNoOfFormants' | |
345 vars = sscanf( str3( spaces(1) : end ), '%f', inf ); | |
346 outputValues = stripOutFormantValues( vars, outputValues ); | |
347 end | |
348 | |
349 if( getRobustFormants ) | |
350 spaces = strfind( str4, ' ' ); % remove the string 'maxNoOfFormants' | |
351 vars = sscanf( str4( spaces(1) : end ), '%f', inf ); | |
352 outputValues = stripOutFormantValues( vars, outputValues ); | |
353 end | |
354 | |
355 [m n] = size( data ); | |
356 % sometimes the 'all' formants command gives us fewer formants than | |
357 % usual. If this is the case,then we will have to pad with zeros | |
358 % for now. | |
359 if( n > length( outputValues ) ) | |
360 lenDiff = n - length( outputValues ); | |
361 outputValues = [ outputValues zeros( 1, lenDiff ) ]; | |
362 elseif( n < length( outputValues ) ) | |
363 lenDiff = length( outputValues ) - n; | |
364 outputValues = [ outputValues zeros( 1, lenDiff ) ]; | |
365 end | |
366 | |
367 data( fileCount, : ) = outputValues; | |
368 | |
369 end | |
370 lineCount = lineCount + 1; | |
371 | |
372 end | |
373 fclose(inputFileID); | |
374 | |
375 % ------------ apply the k-means classifier ------------------------ | |
376 | |
377 noOfClusters = 2; % we are only trying to identify positive and negative emotions | |
378 | |
379 | |
380 [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,... | |
381 'start', 'sample', 'Distance', 'cityblock'); | |
382 | |
383 %display results grouped by emotion | |
384 fprintf( masterFileOutputID, '\n Emotion grouping \n'); | |
385 fprintf( masterFileOutputID, 'cityblock \n'); | |
386 [ groupStats, groupNames ] = processKMeansResults( 'cityblock', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG ); | |
387 [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID, 'cityblock' ); | |
388 | |
389 | |
390 fprintf( masterFileOutputID, 'sqEuclidean \n'); | |
391 [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,... | |
392 'start', 'sample', 'Distance', 'sqEuclidean'); | |
393 | |
394 [ groupStats, groupNames ] = processKMeansResults( 'sqEuclidean', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG ); | |
395 [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID, 'sqEuclidean' ); | |
396 | |
397 | |
398 fprintf( masterFileOutputID, 'cosine \n'); | |
399 [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,... | |
400 'start', 'sample', 'Distance', 'cosine'); | |
401 | |
402 [ groupStats, groupNames ] = processKMeansResults( 'cosine', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG ); | |
403 [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID,'cosine' ); | |
404 | |
405 | |
406 fprintf( masterFileOutputID, 'correlation \n'); | |
407 [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,... | |
408 'start', 'sample', 'Distance', 'correlation'); | |
409 | |
410 [ groupStats, groupNames ] = processKMeansResults( 'correlation', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG ); | |
411 [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID, 'correlation' ); | |
412 | |
413 | |
414 fprintf( masterFileOutputID, '\n' ); | |
415 fclose( masterFileOutputID ); | |
416 | |
417 end | |
418 | |
419 %------------------------------------------------------------------ | |
420 | |
421 function [ outputValues ] = stripOutFormantValues( vars, outputValues ) | |
422 | |
423 noOfFormantValues = length( vars ) - 1; % gives the number of formant arguments only | |
424 noOfFormants = vars(1); | |
425 % there are 12 measurements for the mean of all formants (so the number | |
426 % of formants is not important) for each formant measurement. | |
427 if( noOfFormants ~= (noOfFormantValues-12)/24 ) | |
428 disp('EEK!'); | |
429 pause; | |
430 else | |
431 outputValues = [ outputValues vars( 2:end )' ]; | |
432 end | |
433 | |
434 end | |
435 | |
436 %------------------------------------------------------------------- |