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

Discrete-Time Fourier Series

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

Perceiving Loudness, Pitch, and Location

Perceiving Loudness, Pitch, and Location

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...

Discrete-time Fourier transform

Discrete-time Fourier transform

The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...

Sampling Continuous Time Signal

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...

Linear Approximation in Frequency Domain

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....

You might also read

Related Articles

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

Sort by

Same author

Brain-scale theta band functional connectome as signature of slow breathing and breath-hold phases.

Computers in biology and medicine·2024

Same author

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.

Frontiers in neuroscience·2024

Same author

Under-resourced dialect identification in Ao using source information.

The Journal of the Acoustical Society of America·2022

Same author

Increased Entropy of Gamma Oscillations in the Frontal Region during Meditation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021

Same author

Midline EEG Functional Connectivity As Biomarker for Conscious States in Sleep and Wakefulness.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021

Same author

Brain Functional Connectivity as Biomarker for Propofol-Induced Alterations of Consciousness.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021

Search research articles

Related Experiment Video

Updated: Apr 9, 2026

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

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

Published on: September 27, 2024

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification.

A G Ramakrishnan¹, B Abhiram¹, S R Mahadeva Prasanna²

¹Department of Electrical Engineering, Indian Institute of Science, Bangalore 560012, India ramkiag@ee.iisc.ernet.in, abhiram1989@gmail.com.

The Journal of the Acoustical Society of America

|June 22, 2015

Summary

This study introduces a new voice characterization method using pitch synchronous discrete cosine transform for speaker identification. This technique shows promise, improving accuracy when combined with existing methods.

More Related Videos

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

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

Published on: August 9, 2024

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Apr 9, 2026

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

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

Published on: September 27, 2024

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

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

Published on: August 9, 2024

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

Speech processing
Signal analysis
Biometrics

Background:

Speaker identification (SID) relies on unique voice characteristics.
Existing voice source (VS) features have limitations.
Novel feature extraction methods are needed for improved SID accuracy.

Purpose of the Study:

To propose and evaluate a novel voice source characterization method for speaker identification.
To assess the effectiveness of pitch synchronous discrete cosine transform (PS DCT) on integrated linear prediction residual (ILPR) as a feature vector.
To compare the proposed method against existing voice source features and evaluate its performance in fusion scenarios.

Main Methods:

Characterization of the voice source (VS) signal using pitch synchronous (PS) discrete cosine transform (DCT).
Utilizing the integrated linear prediction residual (ILPR) as the VS estimate.
Evaluating the PS DCT of ILPR as a feature vector for speaker identification (SID) using Gaussian mixture model (GMM) classifiers.

Main Results:

The proposed PS DCT of ILPR performs comparably to existing VS-based features on TIMIT and YOHO databases.
Fusion with MFCC features using a GMM classifier on the NIST 2003 database resulted in a 12% absolute improvement in identification accuracy.
The proposed characterization demonstrates significant potential for speaker identification studies.

Conclusions:

The pitch synchronous discrete cosine transform of the integrated linear prediction residual is an effective feature for speaker identification.
This novel feature shows promise, especially when fused with established methods like MFCC.
The proposed method offers a valuable contribution to the field of speaker identification research.