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FIG. 5 TIME FREQUENCY REPRESENTATIONS OF: A) PURE TONAL VOCALIZATION (MULTITONAL), B) PURE PULSED VOCALIZATION AND C) JAWCLAP VOCALIZATION
TABLE 2 FEATURE SET EMPLOYED IN THE AUTOMATIC DETECTOR Feature Number
v1 v2
Short Description
f0 f 0=∆ f 0 / f 0
Fundamental Frecuency Q-factor of
Power spectral density of the frecuency
f (¿¿ 0) Sx ¿
v3
v4 v5
f1 f 1=∆ f 1 / f 1
Fundamental Frecuency Q-factor of
Power spectral density of the frecuency
f (¿¿ 1) Sx¿
v6
v7 v8
Fundamental Frecuency Q-factor of
v9
v1
TO
)
Other parameters are related to higher order statistics
f2
f 2=∆ f 2 / f 2
Power spectral density of the frequency
v9
FIG. 6 DEFINITION OF FREQUENCY RELATED FEATURES (
f (¿¿ 2) Sx ¿
v 10
Skewness of the vocalization
v 11
Kurtosis of the vocalization
v 12
Autocovariance test of vocalization
v 13
Time reversibility measure of the vocalization
v 14
Voiced/Unvoiced measure
of the vocalization (features and
v 10 ,
v 11 ,
v 12
v 13 ). The preliminary study carried out by the
researchers showed that this statistical information could be useful to identify some particular sound units. The feature
v 10
computes the skewness of
the sound unit. If we see the vocalization as an stochastic process, the skewness is a measure of the asymmetry of the probability distribution and it is computed as described in Eq. (3).
v 10=E
[( ) ] x− μ σ
1 N
3
≈
(
1 N
N
∑ ( x ( n )− ́x ) 3 n=1 N
3 2 2
∑ ( x ( n ) − ́x ) n=1
)
(3)
The operator E [ ∙ ] is the expected value operator and ́x is the arithmetic average. The kurtosis
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