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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
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...
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]...
Properties of Laplace Transform-I01:15

Properties of Laplace Transform-I

The Laplace transform is a powerful mathematical tool used to convert functions from the time domain into the frequency domain, greatly simplifying the analysis and solution of linear time-invariant systems. This transformation is facilitated by several universal properties: Linearity, Time-Scaling, Time-Shifting, and Frequency Shifting.
The Linearity property is foundational to the Laplace transform. It states that the transform of a linear combination of functions is equivalent to the same...
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any finite,...
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...

You might also read

Related Articles

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

Sort by
Same author

Iterative alignment discovery of speech-associated neural activity.

Journal of neural engineering·2024
Same author

Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.

Scientific reports·2024
Same author

Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2023
Same author

Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.

medRxiv : the preprint server for health sciences·2023
Same author

Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection.

Scientific data·2023
Same author

Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.

IEEE journal of translational engineering in health and medicine·2023
Same journal

Sibilant differentiation before and after tongue cancer surgery: Acoustics, kinematics and the role of sensorimotor controla).

The Journal of the Acoustical Society of America·2026
Same journal

BioNet-A: Ultrasonic echo representation network for target discrimination using active SONAR.

The Journal of the Acoustical Society of America·2026
Same journal

Empty soft-drink cans and mass-loaded rods: Analogous homework problems from acoustic and mechanical domains.

The Journal of the Acoustical Society of America·2026
Same journal

Erratum: Statistical wave field theory: Anisotropic wave fields under Neumann's boundary condition [J. Acoust. Soc. Am. 159(3), 2265-2280 (2026)].

The Journal of the Acoustical Society of America·2026
Same journal

On the modification of tip leakage noise sources by porous treatment.

The Journal of the Acoustical Society of America·2026
Same journal

An educational opportunity: Acoustics in an empty room.

The Journal of the Acoustical Society of America·2026
See all related articles

Related Experiment Video

Updated: May 16, 2026

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

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

Temporal resolution analysis in frequency domain linear prediction.

Sriram Ganapathy1, Hynek Hermansky

  • 1IBM T. J. Watson Research Center, Yorktown Heights, New York 10562, USA. ganapath@us.ibm.com

The Journal of the Acoustical Society of America
|November 14, 2012
PubMed
Summary
This summary is machine-generated.

This study enhances Frequency Domain Linear Prediction (FDLP) for modeling speech envelopes. Improved FDLP techniques lead to better phoneme recognition, especially in noisy conditions.

More Related Videos

A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

Related Experiment Videos

Last Updated: May 16, 2026

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

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

Area of Science:

  • Signal Processing
  • Speech Recognition
  • Machine Learning

Background:

  • Frequency Domain Linear Prediction (FDLP) models Hilbert envelopes using autoregressive techniques.
  • Temporal resolution is a critical factor in FDLP model performance, particularly for speech analysis.

Purpose of the Study:

  • Investigate the resolution properties of the FDLP model.
  • Enhance FDLP envelope resolution for improved performance in noisy and reverberant speech.
  • Develop robust features for phoneme recognition using high-resolution FDLP envelopes.

Main Methods:

  • Analysis of FDLP model resolution using synthetic signals with impulses in noise.
  • Systematic study of factors affecting temporal resolution in FDLP.
  • Derivation of robust features from high-resolution FDLP envelopes for phoneme recognition.

Main Results:

  • Identified key factors influencing FDLP temporal resolution.
  • Demonstrated significant improvements in FDLP envelope resolution in noisy speech.
  • FDLP features derived from high-resolution envelopes yielded substantial gains in phoneme recognition accuracy.

Conclusions:

  • The study successfully improved the temporal resolution of FDLP envelopes.
  • High-resolution FDLP features offer a robust solution for phoneme recognition in challenging acoustic environments.
  • This work advances the application of FDLP in robust speech processing.