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

Larynx01:21

Larynx

2.4K
The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids,...
2.4K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

485
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
485
Auditory Perception01:17

Auditory Perception

650
The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
650
Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

908
Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
908
Survey Safety01:28

Survey Safety

204
Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
204
Hearing01:31

Hearing

53.7K
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.
53.7K

You might also read

Related Articles

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

Sort by
Same author

A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution.

Scientific data·2023
Same author

From Corrective to Predictive Maintenance-A Review of Maintenance Approaches for the Power Industry.

Sensors (Basel, Switzerland)·2023
Same author

Time Signature Detection: A Survey.

Sensors (Basel, Switzerland)·2021
Same author

Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data.

Sensors (Basel, Switzerland)·2020
Same author

Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment.

Molecules (Basel, Switzerland)·2019
Same author

The expanded invasive weed optimization metaheuristic for solving continuous and discrete optimization problems.

TheScientificWorldJournal·2014
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 6, 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

495

Singing Voice Detection: A Survey.

Ramy Monir1, Daniel Kostrzewa1, Dariusz Mrozek1

  • 1Department of Applied Informatics, Silesian University of Technology, 44-100 Gliwice, Poland.

Entropy (Basel, Switzerland)
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

This study surveys singing voice detection techniques, comparing classical and advanced methods like convolutional LSTM and GRU-RNN. State-of-the-art algorithms show impressive results on public datasets for vocal detection tasks.

Keywords:
Mel-frequency cepstrum coefficientsdatasetsdeep learning modelshidden Markov modelsmusic information retrievalperceptual linear predictionshort-time Fourier transformsinging voice detectionsupport vector machinesvocal detection

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

1.7K
fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
11:15

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

Published on: May 23, 2017

7.3K

Related Experiment Videos

Last Updated: Oct 6, 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

495
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

1.7K
fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
11:15

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

Published on: May 23, 2017

7.3K

Area of Science:

  • Music Information Retrieval
  • Signal Processing
  • Machine Learning

Background:

  • Singing voice detection (SVD) is essential for music analysis tasks.
  • Accurate SVD improves downstream applications like lyric alignment and melody extraction.

Purpose of the Study:

  • To provide a comprehensive survey of singing voice detection techniques.
  • To investigate both traditional and state-of-the-art SVD algorithms.
  • To compare the performance of different SVD methods.

Main Methods:

  • Review of classical and modern SVD algorithms.
  • Focus on deep learning models like Convolutional Long Short-Term Memory (ConvLSTM) and Gated Recurrent Unit Recurrent Neural Networks (GRU-RNN).
  • Comparative analysis using established datasets (Jamendo, RWC).

Main Results:

  • Long-term recurrent convolutional networks achieve high performance on public datasets.
  • Deep learning approaches demonstrate significant advancements in SVD accuracy.
  • Dataset-specific performance variations are observed across different methods.

Conclusions:

  • SVD is a critical component in music information retrieval systems.
  • Advanced deep learning models offer superior performance for singing voice detection.
  • Further research can refine SVD for diverse musical contexts and datasets.