Novel data collection tools lead to richer datasets that e.g. allow for data-mining

Prediction of speech recognition by hearing-aid users: the syllable-constituent, contextual model of speech perception

Speech perception by hearing aid (HA) users has been evaluated in a database that includes up to 45 hours of testing their aided abilities to recognize syllabic constituents of speech, and words in meaningful sentences, under both masked (eight-talker babble) and quiet conditions.

Continue Reading Prediction of speech recognition by hearing-aid users: the syllable-constituent, contextual model of speech perception

Administration by telephone of US National Hearing Test to 150,000+ persons

The U.S. National Hearing Test has now been taken by over 150,000 people and this extensive database provides reliable estimates of the distribution of hearing loss for people who voluntarily take a digits-in-noise test by telephone.

Continue Reading Administration by telephone of US National Hearing Test to 150,000+ persons

Audiological classification performance based on audiological measurements and Common Audiological Functional Parameters (CAFPAs)

Towards the development of a diagnostic supporting tool in audiology, the Common Audiological Functional Parameters (CAFPAs) were shown to be similarly suitable for audiological finding classification as combinations of typical audiological measurements, and thereby provide the potential to combine different audiological databases.

Continue Reading Audiological classification performance based on audiological measurements and Common Audiological Functional Parameters (CAFPAs)

Use of air conduction thresholds to predict bone conduction asymmetry and air-bone gap

This study used machine learning methods to predict bone conduction abnormalities from air conduction pure tone audiometric thresholds.

Continue Reading Use of air conduction thresholds to predict bone conduction asymmetry and air-bone gap

Predicting abnormal hearing difficulty in noise in ‘normal’ hearers using standard audiological measures

This study used machine learning models trained on otoacoustic emissions and audiometric thresholds to predict self-reported difficulty hearing in noise in normal hearers.

Continue Reading Predicting abnormal hearing difficulty in noise in ‘normal’ hearers using standard audiological measures

Predicting Hearing Aid Fittings Based on Audiometric and Subject-Related Data: A Machine Learning Approach

A machine learning model is trained on real-world fitting data to predict the user's individual gain based on audiometric and further subject-related data, such as age, gender, and the acoustic environments.

Continue Reading Predicting Hearing Aid Fittings Based on Audiometric and Subject-Related Data: A Machine Learning Approach

Aladdin: Automatic LAnguage-independent Development of the Digits-In-Noise test

The Automatic LAnguage-independent Development of the Digits-In-Noise test (Aladdin)-project aims to create a fully automatic test development procedure for digit-in-noise hearing tests in various languages and for different target populations.

Continue Reading Aladdin: Automatic LAnguage-independent Development of the Digits-In-Noise test
The critical role of computing infrastructure in computational audiology
The nine stages of the machine learning workflow.

The critical role of computing infrastructure in computational audiology

The rise of new digital tools for collecting data on scales never before seen in our field coupled with new modeling techniques from deep learning requires us to think about what computational infrastructure we need in order to fully enjoy the benefits and mitigate associated barriers.

Continue Reading The critical role of computing infrastructure in computational audiology
Computational Audiology: new ways to address the global burden of hearing loss
Source: https://www.stripepartners.com/our_writing_article/the-age-of-the-ear/

Computational Audiology: new ways to address the global burden of hearing loss

Computational audiology, the augmentation of traditional hearing health care by digital methods, has potential to dramatically advance audiological precision and efficiency to address the global burden of hearing loss.

Continue Reading Computational Audiology: new ways to address the global burden of hearing loss