Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sound Waves: Resonance

Sound Waves: Resonance

Resonance is produced depending on the boundary conditions imposed on a wave. Resonance can be produced in a string under tension with symmetrical boundary conditions (i.e., has a node at each end). A node is defined as a fixed point where the string does not move. The symmetrical boundary conditions result in some frequencies resonating and producing standing waves, while other frequencies interfere destructively. Sound waves can resonate in a hollow tube, and the frequencies of the sound...

Perception of Sound Waves

Perception of Sound Waves

The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...

Modes of Standing Waves - I

Modes of Standing Waves - I

A close look at earthquakes provides evidence for the conditions appropriate for resonance, standing waves, and constructive and destructive interference. A building may vibrate for several seconds with a driving frequency matching the building's natural frequency of vibration; this produces a resonance that results in one building collapsing while the neighboring buildings do not. Often, buildings of a certain height are devastated, while other taller buildings remain intact. This...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

You might also read

Related Articles

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

Sort by

Same author

Automatic Classification of Functional Dysphonia Using Voc2Vec Speech Representations.

Journal of voice : official journal of the Voice Foundation·2026

Same author

"Metal" and "Density" in Phonation according to Complete Vocal Technique: Double-Case Study with Special Reference to Vocal Efficiency and Economy.

Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP)·2026

Same author

Automatic detection of Parkinsonian speech using wavelet scattering features.

JASA express letters·2025

Same author

Automatic Classification of Strain in the Singing Voice Using Machine Learning.

Journal of voice : official journal of the Voice Foundation·2025

Same author

The machine learning-based prediction of the sound pressure level from pathological and healthy speech signals.

The Journal of the Acoustical Society of America·2025

Same author

Effects of Spoken Phones and Patient Characteristics on Respiratory Aerosol Emission.

Journal of voice : official journal of the Voice Foundation·2025

Same journal

Amplitude-invariant phase masking for coherence recovery in scattered wavefields.

JASA express letters·2026

Same journal

Detecting continuous and discrete frequency changes as a function of spectral resolvability and modulation rate.

JASA express letters·2026

Same journal

Bearings-only acoustic source localization method using two distributed gliders and deep ocean experimental validation in the South China Sea.

JASA express letters·2026

Same journal

Block-sparse enhancement and detection of envelope modulation on noise for ship radiated noise.

JASA express letters·2026

Same journal

Predicting acoustic field with a separate variable ocean physics-informed neural network.

JASA express letters·2026

Same journal

Extending Sottek Hearing Model loudness to estimate partially-masked sound qualities of loudness, tonality, and sharpness.

JASA express letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2025

Hemi-laryngeal Setup for Studying Vocal Fold Vibration in Three Dimensions

Hemi-laryngeal Setup for Studying Vocal Fold Vibration in Three Dimensions

Published on: November 25, 2017

Classification of phonation types in singing voice using wavelet scattering network-based features.

Kiran Reddy Mittapalle¹, Paavo Alku¹

¹Department of Information and Communications Engineering, Aalto University, FI-00076 Espoo, Finlandkiran.r.mittapalle@aalto.fi, paavo.alku@aalto.fi.

JASA Express Letters

|June 7, 2024

Summary

This summary is machine-generated.

Wavelet scattering networks (WSN) improve singing voice phonation classification accuracy by over 9%. These novel features capture pitch, formants, and timbre, enhancing singing style identification.

More Related Videos

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Published on: December 2, 2011

Related Experiment Videos

Last Updated: Jun 24, 2025

Hemi-laryngeal Setup for Studying Vocal Fold Vibration in Three Dimensions

Hemi-laryngeal Setup for Studying Vocal Fold Vibration in Three Dimensions

Published on: November 25, 2017

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Published on: December 2, 2011

Area of Science:

Acoustics
Signal Processing
Music Information Retrieval

Background:

Automatic classification of phonation types is crucial for identifying singing styles.
Existing methods may not fully capture the nuances of vocal production in singing.

Purpose of the Study:

To propose and evaluate wavelet scattering network (WSN)-based features for classifying phonation types in singing voice.
To assess the effectiveness of WSN features in improving classification accuracy compared to current state-of-the-art methods.

Main Methods:

Utilized wavelet scattering networks (WSN), inspired by auditory physiology, to extract acoustic features.
Features extracted by WSN characterize pitch, formants, and timbre.
Compared WSN-based features against state-of-the-art features for phonation type classification.

Main Results:

WSN-based features effectively captured discriminative information across different phonation types.
The proposed WSN features demonstrated a significant improvement in phonation classification accuracy.
An improvement of at least 9% in classification accuracy was achieved compared to existing features.

Conclusions:

Wavelet scattering networks offer a powerful approach for phonation type classification in singing voice.
WSN-based features enhance the accuracy of singing style identification.
This method provides a robust and effective tool for analyzing singing voice characteristics.