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

Properties of Fourier Transform I01:21

Properties of Fourier Transform I

The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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...
Fast Fourier Transform01:10

Fast Fourier Transform

The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
Properties of Fourier series II01:21

Properties of Fourier series II

Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
A function f(t) is...

You might also read

Related Articles

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

Sort by
Same author

Assessing accuracy of resonances obtained with reassigned spectrograms from the "ground truth" of physical vocal tract models.

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

The beginning of time-frequency analysis.

The Journal of the Acoustical Society of America·2022
Same author

Formants are easy to measure; resonances, not so much: Lessons from Klatt (1986).

The Journal of the Acoustical Society of America·2022
Same author

Consonant voicing in the Buckeye corpus.

The Journal of the Acoustical Society of America·2021
Same author

Learnability theory.

Wiley interdisciplinary reviews. Cognitive science·2015
Same author

Accuracy of formant measurement for synthesized vowels using the reassigned spectrogram and comparison with linear prediction.

The Journal of the Acoustical Society of America·2010

Related Experiment Video

Updated: Jul 5, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Phonetic applications of the time-corrected instantaneous frequency spectrogram.

Sean A Fulop1

  • 1Department of Linguistics, California State University Fresno, Fresno., Calif. 93740-8001 USA. sfulop@csufresno.edu

Phonetica
|April 19, 2008
PubMed
Summary

A novel time-corrected instantaneous frequency (TCIF) spectrogram precisely images acoustic signals, improving speech analysis. This technique enhances the visualization of formants and pitch, aiding research in speech production and aeroacoustics.

More Related Videos

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

Related Experiment Videos

Last Updated: Jul 5, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

Area of Science:

  • Acoustic Phonetics
  • Speech Science
  • Signal Processing

Background:

  • Conventional spectrograms suffer from blurring and smearing, hindering precise analysis of acoustic signals.
  • Accurate visualization of signal components like formants is crucial for understanding speech production.

Purpose of the Study:

  • To describe a time-corrected instantaneous frequency (TCIF) spectrogram imaging technique.
  • To explore the applications of TCIF spectrograms in acoustic phonetics.
  • To present a post-processing method for isolating specific signal components.

Main Methods:

  • Development and application of a time-corrected instantaneous frequency (TCIF) spectrogram.
  • Utilizing wideband analysis to observe glottal pulsations for formant accuracy.
  • Employing a post-processing technique for isolating signal components and impulsive events.
  • Demonstrating narrowband analysis for pitch tracking.

Main Results:

  • The TCIF spectrogram precisely locates signal components, overcoming limitations of conventional methods.
  • Formants of vowels and resonants are accurately visualized by observing short-time-scale glottal pulsations.
  • A post-processing technique effectively isolates formants and impulsive events.
  • Narrowband analysis facilitates straightforward pitch tracking.

Conclusions:

  • The TCIF spectrogram offers unparalleled precision in imaging acoustic signals for phonetic analysis.
  • This technique provides evidence supporting recent theories and simulations of aeroacoustic phenomena in speech.
  • TCIF spectrograms significantly enhance the study of speech production mechanisms and characteristics.