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

Auditory Pathway01:15

Auditory Pathway

5.6K
Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
When viewed cross-sectionally, the cochlea reveals the scala vestibuli and scala tympani flanking...
5.6K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

307
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...
307
Sample Handling01:02

Sample Handling

133
Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
133
Hearing01:31

Hearing

52.7K
When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
52.7K
Design Example01:23

Design Example

350
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
350

You might also read

Related Articles

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

Sort by
Same author

Genetic and biotechnological potential of thermophilic Streptomyces sp. isolated from Baikal freshwater psychrophilic sponge.

Scientific reports·2025
Same author

A low-angle trans-magendie foraminal approach to the fourth ventricle and dorsal brainstem.

Neurosurgical review·2025
Same author

Transsylvian Insular Glioma Surgery.

World neurosurgery·2024
Same author

Pineal cyst management: A single-institution experience spanning two decades.

Surgical neurology international·2022
Same author

Median trans-atlanto-occipital membrane microsurgical approach to the posterior cranial fossa without craniotomy.

Journal of neurosurgery·2022
Same author

ExhauFS: exhaustive search-based feature selection for classification and survival regression.

PeerJ·2022

Related Experiment Video

Updated: Aug 8, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

212

Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library.

Volodymyr Khoma1,2, Yuriy Khoma2,3, Vitalii Brydinskyi2,3

  • 1Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary

This study combines speaker diarization and identification to recognize multiple speakers in audio. The group-level approach achieved better identification accuracy than segment-level, though segment-level offers real-time processing.

Keywords:
PyAnnote librarycluster classificationdiarization systemidentification of speakers’ utterancessegmental classification

More Related Videos

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K
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

500

Related Experiment Videos

Last Updated: Aug 8, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

212
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K
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

500

Area of Science:

  • Speech processing
  • Biometrics
  • Machine learning

Background:

  • Speaker diarization segments audio and groups utterances by speaker but doesn't identify them.
  • Speaker identification systems typically require single-speaker audio.
  • Identifying multiple interacting speakers in audio is a significant challenge.

Purpose of the Study:

  • To propose and evaluate two novel architectures for multi-speaker identification systems.
  • To combine diarization and identification methods for enhanced speaker recognition.
  • To investigate segment-level and group-level classification approaches for speaker identification.

Main Methods:

  • Developed speaker identification systems using the PyAnnote open-source framework.
  • Employed a combination of diarization and speaker identification techniques.
  • Conducted four experiments to optimize system performance, focusing on distance functions, clustering, segmentation, and embedding window sizes.
  • Utilized the AMI Corpus for performance evaluation.

Main Results:

  • The group-level classification architecture demonstrated superior speaker identification accuracy compared to the segment-level approach.
  • The segment-level approach offers the advantage of real-time processing capabilities.
  • Experimental results guided the selection of optimal distance functions, clustering, segmentation algorithms, and embedding window sizes.

Conclusions:

  • Combined diarization and identification methods effectively address multi-speaker recognition challenges.
  • Group-level classification is more accurate for speaker identification, while segment-level is suitable for real-time applications.
  • The proposed architectures provide a robust framework for multi-speaker identification in complex audio environments.