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

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...
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...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

You might also read

Related Articles

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

Sort by
Same author

Isolation and characterization of <i>Pseudarthrobacter cremeus</i> sp. nov. and <i>Ideonella flava</i> sp. nov. from mountain soil.

International journal of systematic and evolutionary microbiology·2026
Same author

Phylogenomic Characterization of Hydrogenophaga sedimenti sp. nov. and Larkinella fluvii sp. nov. Isolated from Riverbed Soil.

Current microbiology·2026
Same author

An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.

Science advances·2026
Same author

Small Underwater Objects 3D Point Cloud Dataset Using Mechanical Scanning Sonar.

Scientific data·2026
Same author

<i>Fulvimarina fulva</i> sp. nov. and <i>Fulvimarina thalattae</i> sp. nov., isolated from seawater off Ganghwa Island, South Korea.

International journal of systematic and evolutionary microbiology·2026
Same author

<i>Oceanimonas aquatica</i> sp. nov. and <i>Arenibacter flavimaris</i> sp. nov., isolated from seawater.

International journal of systematic and evolutionary microbiology·2026
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

A constrained sequential EM algorithm for speech enhancement.

Sunho Park1, Seungjin Choi

  • 1Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|April 29, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel constrained sequential EM algorithm for speech enhancement, outperforming traditional Kalman filter methods. The approach effectively handles non-Gaussian noise, improving clean speech estimation from contaminated signals.

More Related Videos

Electrically Evoked Stapedius Reflex Measurements in Cochlear Implantation and Its Application in the Postoperative Fitting Process
07:00

Electrically Evoked Stapedius Reflex Measurements in Cochlear Implantation and Its Application in the Postoperative Fitting Process

Published on: June 21, 2024

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Related Experiment Videos

Last Updated: Jun 28, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Electrically Evoked Stapedius Reflex Measurements in Cochlear Implantation and Its Application in the Postoperative Fitting Process
07:00

Electrically Evoked Stapedius Reflex Measurements in Cochlear Implantation and Its Application in the Postoperative Fitting Process

Published on: June 21, 2024

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Area of Science:

  • Signal Processing
  • Machine Learning
  • Audio Engineering

Background:

  • Speech enhancement aims to recover clean speech from noisy signals.
  • Probabilistic inference is key, but traditional methods like Kalman filters are limited to Gaussian distributions.
  • Non-Gaussian characteristics of speech require advanced modeling.

Purpose of the Study:

  • To develop an advanced speech enhancement method.
  • To address limitations of Gaussian-based models in speech processing.
  • To improve the estimation of clean speech from noisy observations.

Main Methods:

  • Utilized a generalized auto-regressive (GAR) model to capture non-Gaussian speech characteristics.
  • Implemented a constrained sequential Expectation-Maximization (EM) algorithm.
  • Employed Rao-Blackwellized particle filters (RBPFs) in the E-step and sequential parameter updates in the M-step with positivity constraints on noise variance.

Main Results:

  • The proposed method demonstrated superior performance in sequential speech enhancement.
  • Numerical experiments confirmed the effectiveness of the GAR model and constrained sequential EM algorithm.
  • Achieved higher accuracy in estimating clean speech compared to Kalman filter-based approaches.

Conclusions:

  • The constrained sequential EM algorithm with RBPFs offers a robust solution for non-Gaussian speech enhancement.
  • This method advances the state-of-the-art in signal processing for audio applications.
  • The approach provides a significant improvement for real-world noisy speech scenarios.