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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

10.3K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
10.3K
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.4K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
2.4K
Blind Procedures02:07

Blind Procedures

13.9K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.9K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

2.3K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
2.3K

You might also read

Related Articles

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

Sort by
Same author

A comparison of delayed self-heterodyne interference measurement of laser linewidth using Mach-Zehnder and Michelson interferometers.

Sensors (Basel, Switzerland)·2011
Same author

Automatic estimation of voice onset time for word-initial stops by applying random forest to onset detection.

The Journal of the Acoustical Society of America·2011
Same author

Laser ultrasonic evaluation of human dental enamel during remineralization treatment.

Biomedical optics express·2011
Same author

Noncontact, nondestructive elasticity evaluation of sound and demineralized human dental enamel using a laser ultrasonic surface wave dispersion technique.

Journal of biomedical optics·2009
Same author

Laser ultrasonic surface wave dispersion technique for non-destructive evaluation of human dental enamel.

Optics express·2009
Same author

High-power figure-of-eight fiber laser with passive sub-ring loops for repetition rate control.

Optics express·2009
Same journal

Interaction of near-wall bubble arrays with acoustic waves induced by an oscillating rigid wall.

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

Ultra-broadband underwater acoustic projector based on transverse resonance orthogonal beam (TROB) mode and acoustic matching layer technique.

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

Fine-scale quantitative analysis of bowhead whale (Balaena mysticetus) song shows varying stability of song types.

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

High-resolution depth estimation for multiple wideband sources in deep sea via sparse Bayesian learninga).

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

Depression markers in speech: An approach based on tract variables dynamics.

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

The oyster toadfish (Opsanus tau) alters active and diurnal calling amid vessel noise in New York City.

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

Related Experiment Video

Updated: Apr 17, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.1K

Blind phone segmentation based on spectral change detection using Legendre polynomial approximation.

Dac-Thang Hoang1, Hsiao-Chuan Wang2

  • 1Department of Electrical Engineering, EECS Building, Room 719, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu 30013, Taiwan.

The Journal of the Acoustical Society of America
|February 21, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel automatic phone segmentation method using Legendre polynomial coefficients to detect spectral changes in speech signals. The approach achieves comparable or superior accuracy to existing techniques for phone boundary detection.

More Related Videos

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

12.0K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.5K

Related Experiment Videos

Last Updated: Apr 17, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.1K
Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

12.0K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.5K

Area of Science:

  • Speech Processing
  • Acoustic Phonetics
  • Signal Analysis

Background:

  • Phone segmentation is crucial for speech analysis, dividing continuous speech into discrete phone units.
  • Existing methods often require prior knowledge of speech content, limiting their applicability.
  • Accurate phone boundary detection is essential for various speech technology applications.

Purpose of the Study:

  • To propose an automatic phone segmentation method that does not require prior knowledge of speech content.
  • To develop a robust algorithm for detecting phone boundaries by analyzing spectral changes.
  • To improve the accuracy and efficiency of phone segmentation in continuous speech signals.

Main Methods:

  • Representing speech signal spectrum using band-energies.
  • Approximating band-energy curve segments with Legendre polynomial expansion.
  • Detecting spectral changes indicative of phone boundaries by monitoring Legendre polynomial coefficients.
  • Implementing a two-step algorithm for phone boundary detection, including recovery of missed boundaries.

Main Results:

  • The proposed method effectively detects phone boundaries by analyzing variations in Legendre polynomial coefficients.
  • A two-step algorithm successfully identifies boundaries, with a second step recovering boundaries missed in the first.
  • Evaluation on the TIMIT corpus demonstrates the method's accuracy is comparable or superior to previous approaches.

Conclusions:

  • The developed automatic phone segmentation method offers a content-independent approach.
  • Legendre polynomial approximation provides an effective means to characterize spectral properties for boundary detection.
  • The method shows promising results, comparable or exceeding existing techniques in accuracy for phone segmentation.