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Interference: Path Lengths

Interference: Path Lengths

Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...

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

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A Binaural Grouping Model for Predicting Speech Intelligibility in Multitalker Environments.

Jing Mi¹, H Steven Colburn²

¹Boston University, Boston, MA, USA jingmi@bu.edu.

Trends in Hearing

|October 5, 2016

Summary

This study introduces a new binaural model for speech intelligibility, focusing on grouping and target sound selection. The model accurately predicts speech reception thresholds, aiding hearing device development.

Keywords:

EC model binaural hearing grouping

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Area of Science:

Auditory Neuroscience
Signal Processing
Speech Perception

Background:

Spatial separation of speech maskers significantly improves intelligibility.
Binaural hearing research aims to understand brain mechanisms and enhance hearing aids.
Previous models focused on peripheral binaural cues, neglecting binaural grouping benefits.

Purpose of the Study:

To propose a novel binaural model emphasizing grouping for speech source separation.
To quantitatively model the benefits of binaural processing for target sound selection.
To develop a computationally efficient model for predicting speech intelligibility.

Main Methods:

A binaural model using Equalization-Cancellation (EC) processing with a binary decision rule.
Estimation of a time-frequency binary mask based on target signal dominance.
Utilizing the energy change in EC processing as a feature for target dominance detection.

Main Results:

The model successfully predicts speech intelligibility data, matching measured speech reception thresholds.
Predicted intelligibility improvements exceeded some measured data, potentially due to the absence of internal noise in the model.
The proposed model demonstrates straightforward implementation and low computational requirements.

Conclusions:

The developed binaural model effectively captures grouping benefits for speech intelligibility.
The model shows promise for improving signal-processing algorithms in hearing assistive devices.
Further refinement, including internal noise, may enhance model accuracy.