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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Published on: December 10, 2012

Audiogram estimation using Bayesian active learning.

Josef Schlittenlacher¹, Richard E Turner², Brian C J Moore¹

¹Department of Experimental Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, United Kingdom.

The Journal of the Acoustical Society of America

|August 5, 2018

Summary

This summary is machine-generated.

Two novel Bayesian active learning methods rapidly estimate hearing thresholds. These efficient hearing tests provide accurate audiograms in under 4 minutes, improving upon traditional methods.

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Area of Science:

Audiology
Machine Learning
Signal Processing

Background:

Accurate hearing threshold estimation is crucial for diagnosing hearing loss.
Conventional audiometry can be time-consuming and requires specialized equipment.
Bayesian active learning offers a potential for more efficient audiological assessments.

Purpose of the Study:

To develop and evaluate two Bayesian active learning methods for rapid and accurate audiogram estimation.
To compare the performance of these methods against conventional audiograms.
To assess the efficiency and accuracy of the proposed methods in terms of trials and time.

Main Methods:

Developed two Bayesian active learning strategies for audiogram estimation.
One method utilized a tone counting task with Gaussian Process classification and maximum-information sampling.
The second method adapted a Yes/No task to accommodate response lapses.

Main Results:

Both active learning methods generated audiograms comparable to conventional methods.
Threshold estimates were consistently 2-4 dB better (lower) than conventional audiograms.
Accurate audiogram estimates were achieved in less than 50 trials (under 4 minutes).

Conclusions:

Bayesian active learning provides a rapid and accurate approach to hearing threshold estimation.
The developed methods are robust to attention lapses and offer improved efficiency.
The tone counting task demonstrated slightly faster performance for equivalent accuracy.