Esercitazione MATLAB
Reference:
P. Cosi, F. Ferrero e K. Vagges
RAPPRESENTAZIONI ACUSTICHE E UDITIVE DELLE VOCALI
ITALIANE
AIA 95
Piero Cosi
ISTITUTO DI SCIENZE E TECNOLOGIE DELLA COGNIZIONE
SEZIONE DI PADOVA - “FONETICA E DIALETTOLOGIA”
Via G. Anghinoni, 10 - 35121 Padova (Italy)
e-mail: [email protected]
www: http://www.pd.istc.cnr.it
Copyright, 2006 © ISTC-SPFD-CNR
Materiale di Analisi
un piccolo sottoinsieme di:
AIDA CDRom (SI, disk 1 & 2)
bisillabi CV’/ta/ e /t/V’CV
20 maschi + 20 femmine
(18880 vocali)
Metodologia
i
p
t
s( n)
a
samples
energy threshold
computation
d
E(n)
m1
m2
frames
s( n )
s( n )
F3
F3
F2
F2
F1
F1
F1
F2
F1
F3
F2
lpc
F3
lpc
 lpc
 lpc
lpc
t
lpc
t
Parametri di analisi
f0
Fn (n=1,2,3)
Bn (n=1,2,3)
Ln (n=1,2,3)
-
frequenza fondamentale
frequenze formantiche
larghezze di banda
intensità (dB).
Metodologia
p
i
t
a
s( n )
samples
energy threshold
computation

E(n)
m1
m2
frames
Metodologia
Rappresentazioni acustico-uditive
1 :
2 :
3 :
4 :
F1, F2, F3
F1-f0, F2-F1, F3-F2
F1, F2, F3
F1-f0, F2-F1, F3-F2
2



 f   f 
b  7 ln  
  
  1
  650    650 


Hz  bark
(Hz)
(Hz)
(Bark)
(Bark)
1
2




cV1/ta/


female

male

female

male










female
male

male





female



































cV1/ta/





















female
male






female
male

F1, F2, F3



F1
291
394
513
F2
2251
2082
F3
3079
M ale

Female




742
552
447
325
F1
3
1989
1420
949
856
789
F2
2
2752
2669
2532
2569
2528
2529
F3
3







325
F1
339
436
630
875
688
506
360
789
F2
2672
2508
2302
1614
1115
990
838
2529
F3
3595
3158
2999
2697
2712
2606
2466
Female
Discriminant Analysis
1







2








95.4
0.0
1.4
0.0
0.0
3.2
0.0

95.4
0.0
1.7
0.0
0.0
2.9
0.0

0.0
68.8
13.9
17.0
0.0
0.0
0.3

0.0
63.9
17.3
18.6
0.0
0.0
0.3

1.0
22.3
74.8
1.8
0.0
0.0
0.0

0.3
22.1
74.6
3.1
0.0
0.0
0.0

0.0
12.7
0.0
87.3
0.0
0.0
0.0

0.0
13.0
0.0
87.0
0.0
0.0
0.0

0.0
0.0
0.0
0.0
74.3
15.8
9.9

0.0
0.0
0.0
0.0
73.0
17.8
9.3

3.4
0.7
2.1
0.7
21.6
71.3
0.2

2.5
0.7
2.1
0.7
21.0
72.9
0.2

0.0
0.0
0.0
0.0
0.6
0.0
99.4

0.0
0.0
0.0
0.0
2.4
0.0
97.6
m1
81.6
m1
80.6
m2
92.1
m2
91.8
3







4








96.8
0.0
1.4
0.0
0.0
1.7
0.0

96.3
0.0
1.4
0.0
0.0
2.3
0.0

0.0
74.5
11.6
13.7
0.3
0.0
0.0

0.0
70.4
15.0
14.4
0.0
0.0
0.3

0.8
21.8
76.9
0.5
0.0
0.0
0.0

0.5
22.9
75.8
0.8
0.0
0.0
0.0

0.0
10.1
0.0
89.9
0.0
0.0
0.0

0.0
10.6
0.0
89.4
0.0
0.0
0.0

0.0
0.0
0.0
0.0
73.6
17.1
9.3

0.0
0.0
0.0
0.0
70.9
19.5
9.6

4.1
0.9
2.5
0.5
20.1
72.0
0.0

3.6
0.9
2.5
0.7
20.3
71.8
0.2

0.0
0.0
0.0
0.0
1.2
0.0
98.8

0.0
0.0
0.0
0.0
3.1
0.0
97.0
m1
83.2
m1
81.6
m2
93.3
m2
92.7
Software
Computer-Based Exercises for Signal Processing Using MATLAB 5
James H. McClellan, Georgia Institute of Technology
C. Sidney Burrus, Rice University
Alan V. Oppenheim, Massachusetts Institute of Technology
Thomas W. Parks
Ronald W. Schafer, Georgia Institute of Technology
Hans W. Schuessler
Prentice Hall, 1998
Tel: 800-282-0693
Fax: 800-835-5327
Outside North America
Tel: 201-767-4900
Fax: 201-767-5625
ISBN: 0-13-789009-5
Language: English
ftp://ftp.ece.gatech.edu/pub/projects/MATLAB/CmpExDSP/
per l’analisi statistica è stato utilizzato SYSTAT