Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Discrete Fourier Transform01:15

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Masking noise reduces the anti-predator-like response to an acoustic stimulus: Application of Signal Detection Theory to fish behaviour.

PloS one·2025
Same author

Evaluating the performance of automated detection systems for long-term monitoring of delphinids in diverse marine soundscapes.

PloS one·2025
Same author

Sounds of the deep: How input representation, model choice, and dataset size influence underwater sound classification performance.

The Journal of the Acoustical Society of America·2025
Same author

Speed dependence, sources, and directivity of small vessel underwater noise.

The Journal of the Acoustical Society of America·2024
Same author

To Bag or Not to Bag? How AudioMoth-Based Passive Acoustic Monitoring Is Impacted by Protective Coverings.

Sensors (Basel, Switzerland)·2023
Same author

Methods of acoustic gas flux inversion-Investigation into the initial amplitude of bubble excitation.

The Journal of the Acoustical Society of America·2022

Related Experiment Video

Updated: May 29, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

An adaptive filter-based method for robust, automatic detection and frequency estimation of whistles.

A Torbjorn Johansson1, Paul R White

  • 1Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK. torbjorn.johansson@foi.se

The Journal of the Acoustical Society of America
|September 1, 2011
PubMed
Summary

This study introduces an adaptive filter method for detecting and estimating frequencies of whistle calls from animals and humans. The novel approach accurately identifies multiple simultaneous whistles, outperforming spectrogram methods without false alarms.

More Related Videos

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

Related Experiment Videos

Last Updated: May 29, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

Area of Science:

  • Bioacoustics
  • Signal Processing
  • Animal Communication

Background:

  • Whistle calls from birds and marine mammals are crucial for communication and are typically analyzed using spectrograms in the time-frequency domain.
  • Existing spectrogram-based methods face challenges in accurately detecting and tracking complex whistle contours, especially in noisy environments or with multiple simultaneous calls.

Purpose of the Study:

  • To develop and validate an adaptive filter-based method for robust detection and precise frequency estimation of whistle calls.
  • To improve upon existing methods by enhancing frequency tracking capabilities and handling interfering signals.
  • To assess the performance of the proposed method against established spectrogram-based techniques.

Main Methods:

  • An adaptive notch filtering technique was employed for frequency tracking.
  • Novel methods for whistle detection and frequency tracking, incorporating frequency crossings and transient interference management, were developed.
  • Background noise was estimated and compensated for using order statistics and pre-whitening techniques.

Main Results:

  • The proposed adaptive filter method successfully detected more simultaneous whistles than two competing spectrogram-based methods.
  • The method demonstrated no false alarms on simulated and recorded datasets.
  • Complete bottlenose dolphin whistles were accurately extracted, even at high sweep rates.
  • Effectiveness was shown on both marine mammal calls and human whistled utterances.

Conclusions:

  • The adaptive filter-based method offers superior performance for whistle call detection and frequency estimation compared to traditional spectrogram approaches.
  • The developed techniques for frequency tracking and noise compensation enhance the robustness and accuracy of whistle analysis.
  • This method holds significant potential for automated bioacoustic monitoring and analysis of vocalizations in various species.