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Multifractal detrended fluctuation analysis to characterize phase couplings in seahorse (<i>Hippocampus kuda</i>) feeding clicks
10
Citations
47
References
2014
Year
PsychoacousticsBrain MechanismFeeding ClicksSensory SystemsSocial SciencesNeural MechanismMultifractal Spectrum FNeurodynamicsVocal Tract ImagingNoiseAcoustic AnalysisPhase CouplingsHealth SciencesAcoustic EcologyAuditory ModelingBehavioral NeuroscienceFluctuation AnalysisNonlinear PhenomenaSensorimotor IntegrationSpeech AcousticNervous SystemBrain CircuitrySystems NeuroscienceBiologyBioacousticsNeurophysiologyPhysiologyAnimal BehaviorNeuroscienceSpeech PerceptionAnimal VocalizationsAuditory System
Nonlinear phenomena in animal vocalizations fundamentally includes known features, namely, frequency jump, subharmonics, biphonation, and deterministic chaos. In the present study, the multifractal detrended fluctuation analysis (MFDFA) has been employed to characterize the phase couplings revealed in the feeding clicks of Hippocampus kuda yellow seahorse. The fluctuation function Fq(s), generalized Hurst exponent h(q), multifractal scaling exponent τ(q), and the multifractal spectrum f(α) calculated in the procedure followed were analyzed to comprehend the underlying nonlinearities in the seahorse clicks. The analyses carried out reveal long-range power-law correlation properties in the data, substantiating the multifractal behavior. The resulting h(q) spectrum exhibits a distinct characteristic pattern in relation to the seahorse sex and size, and reveals a spectral blind spot in the data that was not possible to detect by conventional spectral analyses. The corresponding multifractal spectrum related width parameter Δh(q) is well clustered, defining the individual seahorse clicks. The highest degree of multifractality is evident in the 18 cm male seahorse, signifying greater heterogeneity. A further comparison between the seahorse body size and weight (wet) with respect to the width parameter Δh(q) and the second-order Hurst exponent h(q=2) underscores the versatility of MFDFA as a robust statistical tool to analyze bioacoustic observations.
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