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Related Concept Videos

Sound Intensity Level00:53

Sound Intensity Level

Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and hence a...
Perceiving Loudness, Pitch, and Location01:21

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 identifying...
Intensity and Pressure of Sound Waves01:05

Intensity and Pressure of Sound Waves

The intensity of sound waves can be related to displacement and pressure amplitudes by using their wave expressions and the definition of intensity. The critical step to achieve this is to write the power delivered by the particles on the wave as the product of force and velocity and simplify the force per unit area as the pressure. The velocity of the medium's particles can be derived from the displacement.
Unlike the time average of a sinusoidal term, which is zero since it is positive and...

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Related Experiment Video

Updated: May 9, 2026

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

A multi-resolution envelope-power based model for speech intelligibility.

Søren Jørgensen1, Stephan D Ewert, Torsten Dau

  • 1Centre for Applied Hearing Research, Department of Electrical Engineering, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. sjor@elektro.dtu.dk

The Journal of the Acoustical Society of America
|July 19, 2013
PubMed
Summary
This summary is machine-generated.

A new multi-resolution speech-based envelope power spectrum model (sEPSM) improves speech intelligibility predictions. This enhanced model accurately estimates signal-to-noise ratios even with fluctuating background noise.

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Last Updated: May 9, 2026

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Published on: August 9, 2024

Area of Science:

  • Acoustics and Signal Processing
  • Auditory Perception
  • Speech Communication

Background:

  • The speech-based envelope power spectrum model (sEPSM) estimates speech intelligibility by calculating the envelope power signal-to-noise ratio (SNRenv).
  • Existing sEPSM models perform well for stationary noise but fail with fluctuating maskers, limiting their application.
  • The standardized Speech Transmission Index (STI) is inadequate for certain noise conditions like spectral subtraction.

Purpose of the Study:

  • To develop and validate a multi-resolution version of the sEPSM (mr-sEPSM) capable of handling both stationary and fluctuating maskers.
  • To assess the mr-sEPSM's effectiveness in predicting speech intelligibility across various challenging acoustic conditions.
  • To confirm the SNRenv as a robust objective metric for speech intelligibility.

Main Methods:

  • A multi-resolution approach was integrated into the sEPSM, enabling SNRenv estimation within temporally segmented, modulation-filter-dependent durations.
  • The mr-sEPSM was evaluated against speech intelligibility data obtained under conditions including stationary noise, fluctuating noise, reverberation, and spectral subtraction.
  • Performance was compared to existing models, highlighting the limitations of the original sEPSM and STI.

Main Results:

  • The multi-resolution sEPSM successfully predicted speech intelligibility in conditions with both stationary and fluctuating interferers.
  • The model demonstrated accuracy for speech distorted by reverberation and spectral subtraction.
  • The findings support the enhanced SNRenv metric's utility for objective intelligibility prediction.

Conclusions:

  • The multi-resolution sEPSM offers a significant advancement over previous models, extending accurate intelligibility prediction to more complex acoustic environments.
  • The SNRenv, particularly when estimated using the multi-resolution approach, is a powerful and versatile objective measure for speech intelligibility.
  • This research provides a more robust tool for evaluating speech communication systems and assistive listening devices.