An important application of passive acoustics is the identification, recognition, and matching of signals by marine species. In this paper, the attention is mainly given for matching of marine species call signals present in the ambient noise data collected during March 2013 in the shallow waters off Goa by an autonomous ambient noise system of vertical linear hydrophone array. In order to know whether both the calls are from same marine species or not, it is decided to carry out the work by considering two cases. The first case uses the call signals with background noise and the second case uses the same call signals which are enhanced by canceling out the background noise. The enhancement of a call signals has been done with short time fourier transform (STFT), logical applications and inverse STFT. These two cases are done with time series data of 50000 sampling rate through the standard Empirical Mode Decomposition method (EMD). The time window taken for EMD analysis of each signal is 2048 and therefore eighteen numbers of classes are formed in each signal. An EMD algorithm decomposes adaptively the signal x(t) into intrinsic mode functions (IMF) and into residue. IMFs are signals with characteristics such as the number of extremes (minima and maxima) and the number of zero-crossings must either equal or must differ by a maximum of one in the whole dataset. The correlation between the intrinsic mode functions of similar classes from two marine species calls is calculated. From these values, the correlation is very poor of the order of 0.4 for noisy call signals which is almost half of enhanced call signal. From the results, it is found that EMD algorithm applied to noisy marine species call signals misleads us to conclude that the two call signals are not from same marine species. Therefore, from the study it is clearly understood that the EMD algorithm should be applied to enhanced call signals to decide whether the call signals are from same marine species or not.
[1]
N. Huang,et al.
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
,
1998,
Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[2]
Norden E. Huang,et al.
A review on Hilbert‐Huang transform: Method and its applications to geophysical studies
,
2008
.
[3]
I. Jánosi,et al.
Empirical mode decomposition and correlation properties of long daily ozone records.
,
2005,
Physical review. E, Statistical, nonlinear, and soft matter physics.
[4]
Pi-Cheng Tung,et al.
Data analysis using a combination of independent component analysis and empirical mode decomposition.
,
2009,
Physical review. E, Statistical, nonlinear, and soft matter physics.
[5]
Chin-Kun Hu,et al.
Empirical mode decomposition and synchrogram approach to cardiorespiratory synchronization.
,
2006,
Physical review. E, Statistical, nonlinear, and soft matter physics.
[6]
Chung-Shi Chiang,et al.
Using Empirical Mode Decomposition for Underwater Acoustic Signals Recognition
,
2007
.
[7]
Dean G. Duffy,et al.
The Application of Hilbert-Huang Transforms to Meteorological Datasets
,
2004
.