Improving spectrogram correlation filters with time-frequency reassignment for bio-acoustic signal classification

Salina Abdul Samad, Aqilah Baseri Huddin

Research output: Contribution to journalArticle

Abstract

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 representations of the call-prints are 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 rates obtained with TF reassignment demonstrates that this is a viable method for bio-acoustic signal classification.

Original languageEnglish
Pages (from-to)62-67
Number of pages6
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Apr 2019

Fingerprint

Correlation Filter
Spectrogram
Acoustics
Classifiers
Animals
Classifier
Margin
Demonstrate
Template
Classify
Target

Keywords

  • Bio-acoustic signal
  • Classification
  • Correlation filter
  • Spectrogram
  • Time-frequency reassignment

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

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abstract = "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 representations of the call-prints are 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 rates obtained with TF reassignment demonstrates that this is a viable method for bio-acoustic signal classification.",
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