Pure-tone audiometry—the process of estimating a person's hearing threshold from “audible” and “inaudible” responses to tones of varying frequency and intensity—is the basis for diagnosing and quantifying hearing loss. By taking a probabilistic modeling approach, both optimal tone selection (in terms of expected information gain) and hearing threshold estimation can be derived through Bayesian inference methods. The performance of probabilistic model-based audiometry methods is directly linked to the quality of the underlying model. In recent years, Gaussian process (GP) models have been shown to provide good results in this context. We present methods to improve the efficiency of GP-based audiometry procedures by improving the underlying m...
Hearing loss afflicts as many as 10 % of our population. Fortunately, tech-nologies designed to alle...
The Auditory Brainstem Response (ABR) plays an important role in diagnosing and managing hearing los...
Hearing healthcare professionals rely on the audiograms produced through pure tone audiometry, among...
Pure-tone audiometry—the process of estimating a person's hearing threshold from “audible” and “inau...
Machine learning approaches to hearing loss estimation can significantly reduce the number of requir...
Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of...
Hearing Aid (HA) algorithms need to be tuned (“fitted”) to match the impairment of each specific pat...
The present invention relates to a new method for effective estimation of signal processing paramete...
Time-efficient hearing tests are important in both clinical practice and research studies. This part...
In this paper we present AIDA, which is an active inference-based agent that iteratively designs a p...
Abstract:This paper presents the results of applying machine learning algorithms to predict hearing ...
Purpose: This article presents a basic exploration of Bayesian inference to inform researchers unfam...
Modern hearing aids use Dynamic Range Compression (DRC) as the primary solution to combat Hearing Lo...
Hearing loss afflicts as many as 10 % of our population. Fortunately, tech-nologies designed to alle...
The Auditory Brainstem Response (ABR) plays an important role in diagnosing and managing hearing los...
Hearing healthcare professionals rely on the audiograms produced through pure tone audiometry, among...
Pure-tone audiometry—the process of estimating a person's hearing threshold from “audible” and “inau...
Machine learning approaches to hearing loss estimation can significantly reduce the number of requir...
Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of...
Hearing Aid (HA) algorithms need to be tuned (“fitted”) to match the impairment of each specific pat...
The present invention relates to a new method for effective estimation of signal processing paramete...
Time-efficient hearing tests are important in both clinical practice and research studies. This part...
In this paper we present AIDA, which is an active inference-based agent that iteratively designs a p...
Abstract:This paper presents the results of applying machine learning algorithms to predict hearing ...
Purpose: This article presents a basic exploration of Bayesian inference to inform researchers unfam...
Modern hearing aids use Dynamic Range Compression (DRC) as the primary solution to combat Hearing Lo...
Hearing loss afflicts as many as 10 % of our population. Fortunately, tech-nologies designed to alle...
The Auditory Brainstem Response (ABR) plays an important role in diagnosing and managing hearing los...
Hearing healthcare professionals rely on the audiograms produced through pure tone audiometry, among...