Open AccessImproving spectrogram correlation filters with time-frequency reassignment for bio-acoustic signal classification
Author(s) -
Salina Abdul Samad,
Aqilah Baseri Huddin
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v14.i1.pp59-64
Subject(s) - spectrogram , classifier (uml) , correlation , pattern recognition (psychology) , artificial intelligence , speech recognition , computer science , filter (signal processing) , signal (programming language) , time–frequency analysis , mathematics , computer vision , geometry , programming language
Spectrogram features have been used to automatically classify animals based on their vocalization. Usually, features are extracted and used as inputs to classifiers to distinguish between species. In this paper, a classifier based on Correlation Filters (CFs) is employed where the input features are the spectrogram image themselves. Spectrogram parameters are carefully selected based on the target dataset in order to obtain clear distinguishing images termed as call-prints. An even better representation of the call-prints is obtained using spectrogram Time-Frequency (TF) reassignment. To demonstrate the application of the proposed technique, two species of frogs are classified based on their vocalization spectrograms where for each species a correlation filter template is constructed from multiple call-prints using the Maximum Margin Correlation Filter (MMCF). The improved accuracy rate obtained with TF reassignment demonstrates that this is a viable method for bio-acoustic signal classification.