Related Experiment Video
Updated: May 20, 2025
05:57
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
Published on: April 8, 2019
6.8K
Classification of Bryde's whale individuals using high-resolution time-frequency transforms and support vector
Jean Baptiste Tary1, Christine Peirce2, Richard W Hobbs2
1Geophysics Section, School of Cosmic Physics, Dublin Institute for Advanced Studies, Dublin D02Y006, Ireland.
The Journal of the Acoustical Society of America
|March 25, 2025
Summary
Bryde's whale vocalizations contain individual-specific information. Advanced Fourier synchrosqueezing transform and support vector machines accurately identified whales from their calls, enabling individual whale studies using ocean-bottom data.
Area of Science:
- Marine bioacoustics
- Animal communication
- Signal processing
Background:
- Whale vocalizations may encode individual identity.
- Bryde's whale calls recorded near Costa Rica Rift exhibit specific spatiotemporal patterns.
- Previous methods for analyzing whale calls had limitations.
Purpose of the Study:
- To assess the suitability of time-frequency characteristics for identifying individual Bryde's whales.
- To develop and validate an advanced signal processing technique for whale call analysis.
- To determine if individual-specific information is present in Bryde's whale vocalizations.
Main Methods:
- Utilized a high-resolution fourth-order Fourier synchrosqueezing transform to extract time-frequency ridges from whale calls.
- Applied support vector machine (SVM) clustering to classify whale call ridges.
- Focused analysis on high-quality calls recorded within 5 km of the source.
Main Results:
- The Fourier synchrosqueezing transform and SVM model achieved an average cross-validation error of approximately 11% and a balanced accuracy of around 86%.
- This advanced method significantly improved classification accuracy compared to standard short-time Fourier transform and k-means clustering.
- High-quality calls yielded more reliable individual identification results.
Conclusions:
- Bryde's whale calls contain potentially individual-specific information.
- The Fourier synchrosqueezing transform coupled with SVM offers a robust method for identifying individual whales from their vocalizations.
- Ocean-bottom hydrophone data can be effectively utilized for studying individual whales.
Related Concept Videos
01:30Classification of Signals
365
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
365
01:26Classification of Systems-I
164
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
164
01:31Classification of Systems-II
131
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
131
01:29Aggregates Classification
293
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
293
01:22Force Classification
1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K
01:08Determination of Expected Frequency
2.1K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.1K