Rapid estimation of high-parameter auditory-filter shapes

Toward parametric Bayesian adaptive procedures for multi-frequency categorical loudness scaling

A series of Bayesian adaptive procedures to estimate loudness growth across a wide frequency range from individual listeners was developed, and these procedures were compared. Simulation experiments were conducted based on multinomial psychometric functions for categorical loudness scaling across ten test frequencies estimated from 61 listeners with normal hearing and 87 listeners with sensorineural hearing loss. Adaptive procedures that optimized the stimulus selection based on the interim estimates of two types of category-boundary models were tested. The first type of model was a phenomenological model of category boundaries adopted from previous research studies, while the other type was a data-driven model derived from a previously collected set of categorical loudness scaling data. An adaptive procedure without Bayesian active learning was also implemented. Results showed that all adaptive procedures provided convergent estimates of the loudness category boundaries and equal-loudness contours between 250 and 8000 Hz. Performing post hoc model fitting, using the data-driven model, on the collected data led to satisfactory accuracies, such that all adaptive procedures tested in the current study, independent of modeling approach and stimulus-selection rules, were able to provide estimates of the equal-loudness-level contours between 20 and 100 phons with root-mean-square errors typically under 6 dB after 100 trials.

Test-retest evaluation of a notched-noise test using consumer-grade mobile audio equipment

OBJECTIVE

The aim of this study was to investigate whether consumer-grade mobile audio equipment can be reliably used as a platform for the notched-noise test, including when the test is conducted outside the laboratory.

DESIGN

Two studies were conducted: Study 1 was a notched-noise masking experiment with three different setups: in a psychoacoustic test booth with a standard laboratory PC; in a psychoacoustic test booth with a mobile device; and in a quiet office room with a mobile device. Study 2 employed the same task as Study 1, but compared circumaural headphones to insert earphones.

STUDY SAMPLE

Nine and ten young, normal-hearing participants completed studies 1 and 2, respectively.

RESULTS

The test-retest accuracy of the notched-noise test on the mobile implementation did not differ from that for the laboratory setup. A possible effect of the earphone design was identified in Study 1, which was corroborated by Study 2, where test-retest variability was smallest when comparing results from experiments conducted using identical acoustic transducers.

CONCLUSIONS

Results and test-retest repeatability comparable to standard laboratory settings for the notched-noise test can be obtained with mobile equipment outside the laboratory.

Changes in audiometric threshold and frequency selectivity correlate with cochlear histopathology in macaque monkeys with permanent noise-induced hearing loss

Exposure to loud noise causes damage to the inner ear, including but not limited to outer and inner hair cells (OHCs and IHCs) and IHC ribbon synapses. This cochlear damage impairs auditory processing and increases audiometric thresholds (noise-induced hearing loss, NIHL). However, the exact relationship between the perceptual consequences of NIHL and its underlying cochlear pathology are poorly understood. This study used a nonhuman primate model of NIHL to relate changes in frequency selectivity and audiometric thresholds to indices of cochlear histopathology. Three macaques (one Macaca mulatta and two Macaca radiata) were trained to detect tones in quiet and in noises that were spectrally notched around the tone frequency. Audiograms were derived from tone thresholds in quiet; perceptual auditory filters were derived from tone thresholds in notched-noise maskers using the rounded-exponential fit. Data were obtained before and after a four-hour exposure to a 50-Hz noise centered at 2 kHz at 141 or 146 dB SPL. Noise exposure caused permanent audiometric threshold shifts and broadening of auditory filters at and above 2 kHz, with greater changes observed for the 146-dB-exposed monkeys. The normalized bandwidth of the perceptual auditory filters was strongly correlated with audiometric threshold at each tone frequency. While changes in audiometric threshold and perceptual auditory filter widths were primarily determined by the extent of OHC survival, additional variability was explained by including interactions among OHC, IHC, and ribbon synapse survival. This is the first study to provide within-subject comparisons of auditory filter bandwidths in an animal model of NIHL and correlate these NIHL-related perceptual changes with cochlear histopathology. These results expand the foundations for ongoing investigations of the neural correlates of NIHL-related perceptual changes.

Application of Bayesian Active Learning to the Estimation of Auditory Filter Shapes Using the Notched-Noise Method

Time-efficient hearing tests are important in both clinical practice and research studies. This particularly applies to notched-noise tests, which are rarely done in clinical practice because of the time required. Auditory-filter shapes derived from notched-noise data may be useful for diagnosis of the cause of hearing loss and for fitting of hearing aids, especially if measured over a wide range of center frequencies. To reduce the testing time, we applied Bayesian active learning (BAL) to the notched-noise test, picking the most informative stimulus parameters for each trial based on nine Gaussian Processes. A total of 11 hearing-impaired subjects were tested. In 20 to 30 min, the test provided estimates of signal threshold as a continuous function of frequency from 500 to 4000 Hz for nine notch widths and for notches placed both symmetrically and asymmetrically around the signal frequency. The thresholds were found to be consistent with those obtained using a 2-up/1-down forced-choice procedure at a single center frequency. In particular, differences in threshold between the methods did not vary with notch width. An independent second run of the BAL test for one notch width showed that it is reliable. The data derived from the BAL test were used to estimate auditory-filter width and asymmetry and detection efficiency for center frequencies from 500 to 4000 Hz. The results agreed with expectations for cochlear hearing losses that were derived from the audiogram and a hearing model.

Individualized estimation of the Speech Intelligibility Index for short sentences: Test-retest reliability

The speech intelligibility index (SII) model was modified to allow individualized parameters. These parameters included the relative weights of speech cues in five octave-frequency bands ranging from 0.25 to 4 kHz, i.e., the band importance function, and the transfer function that allows the SII to generate predictions on speech-recognition scores. A Bayesian adaptive procedure, the quick-band-importance-function (qBIF) procedure, was utilized to enable efficient estimation of the SII parameters from individual listeners. In two experiments, the SII parameters were estimated for 30 normal-hearing adults using Institute of Electrical and Electronics Engineers (IEEE) sentences at speech levels of 55, 65, and 75 dB sound pressure level (in Experiment I) and for 15 hearing-impaired (HI) adult listeners using amplified IEEE or AzBio sentences (in Experiment II). In both experiments, even without prior training, the estimated model parameters showed satisfactory reliability between two runs of the qBIF procedure at least one week apart. For the HI listeners, inter-listener variability in most estimated SII parameters was larger than intra-listener variability of the qBIF procedure.

Visual Field Estimation by Probabilistic Classification

The gold standard clinical tool for evaluating visual dysfunction in cases of glaucoma and other disorders of vision remains the visual field or threshold perimetry exam. Administration of this exam has evolved over the years into a sophisticated, standardized, automated algorithm that relies heavily on specifics of disease processes particular to common retinal disorders. The purpose of this study is to evaluate the utility of a novel general estimator applied to visual field testing. A multidimensional psychometric function estimation tool was applied to visual field estimation. This tool is built on semiparametric probabilistic classification rather than multiple logistic regression. It combines the flexibility of nonparametric estimators and the efficiency of parametric estimators. Simulated visual fields were generated from human patients with a variety of diagnoses, and the errors between simulated ground truth and estimated visual fields were quantified. Error rates of the estimates were low, typically within 2 dB units of ground truth on average. The greatest threshold errors appeared to be confined to the portions of the threshold function with the highest spatial frequencies. This method can accurately estimate a variety of visual field profiles with continuous threshold estimates, potentially using a relatively small number of stimuli.

The effect of speech material on the band importance function for Mandarin Chinese

Speech material influences the relative contributions of different frequency regions to intelligibility for English. In the current study, whether a similar effect of speech material is present for Mandarin Chinese was investigated. Speech recognition was measured using three speech materials in Mandarin, including disyllabic words, nonsense sentences, and meaningful sentences. These materials differed from one another in terms of the amount of contextual information and word frequency. The band importance function (BIF), as defined under the Speech Intelligibility Index (SII) framework, was used to quantify the contributions across frequency regions. The BIFs for the three speech materials were estimated from 16 adults who were native speakers of Mandarin. A Bayesian adaptive procedure was used to efficiently estimate the octave-frequency BIFs for the three materials for each listener. As the amount of contextual information increased, low-frequency bands (e.g., 250 and 500 Hz) became more important for speech recognition, consistent with English. The BIF was flatter for Mandarin than for comparable English speech materials. Introducing the language- and material-specific BIFs to the SII model led to improved predictions of Mandarin speech-recognition performance. Results suggested the necessity of developing material-specific BIFs for Mandarin.

An Analysis of Individual Differences in Recognizing Monosyllabic Words Under the Speech Intelligibility Index Framework

Individual differences in the recognition of monosyllabic words, either in isolation (NU6 test) or in sentence context (SPIN test), were investigated under the theoretical framework of the speech intelligibility index (SII). An adaptive psychophysical procedure, namely the quick-band-importance-function procedure, was developed to enable the fitting of the SII model to individual listeners. Using this procedure, the band importance function (i.e., the relative weights of speech information across the spectrum) and the link function relating the SII to recognition scores can be simultaneously estimated while requiring only 200 to 300 trials of testing. Octave-frequency band importance functions and link functions were estimated separately for NU6 and SPIN materials from 30 normal-hearing listeners who were naïve to speech recognition experiments. For each type of speech material, considerable individual differences in the spectral weights were observed in some but not all frequency regions. At frequencies where the greatest intersubject variability was found, the spectral weights were correlated between the two speech materials, suggesting that the variability in spectral weights reflected listener-originated factors.

Toward Routine Assessments of Auditory Filter Shape

Purpose A Bayesian adaptive procedure, that is, the quick auditory filter (qAF) procedure, has been shown to improve the efficiency for estimating auditory filter shapes of listeners with normal hearing. The current study evaluates the accuracy and test-retest reliability of the qAF procedure for naïve listeners with a variety of ages and hearing status. Method Fifty listeners who were naïve to psychophysical experiments and exhibit wide ranges of age (19-70 years) and hearing threshold (-5 to 70 dB HL at 2 kHz) were recruited. Their auditory filter shapes were estimated for a 15-dB SL target tone at 2 kHz using both the qAF procedure and the traditional threshold-based procedure. The auditory filter model was defined using 3 parameters: (a) the sharpness of the tip portion of the auditory filter, p; (b) the prominence of the low-frequency tail of the filter, 10log( w); and (c) the listener's efficiency in detection, 10log( K). Results The estimated parameters of the auditory filter model were consistent between 2 qAF runs tested on 2 separate days. The parameter estimates from the 2 qAF runs also agreed well with those estimated using the traditional procedure despite being substantially faster. Across the 3 auditory filter estimates, the dependence of the auditory filter parameters on listener age and hearing threshold was consistent across procedures, as well as consistent with previously published estimates. Conclusions The qAF procedure demonstrates satisfactory test-retest reliability and good agreement to the traditional procedure for listeners with a wide range of ages and with hearing status ranging from normal hearing to moderate hearing impairment.

Efficient assessment of the time course of perceptual sensitivity change

Perceptual sensitivity is usually estimated over trials and time intervals, which results in imprecise and biased estimates when it changes rapidly over time. We develop a novel procedure, the quick Change-Detection (qCD) method, to accurately, precisely, and efficiently assess the trial-by-trial time course of perceptual sensitivity change. Based on Bayesian adaptive testing, qCD selects the optimal stimulus, and updates, trial by trial, a joint probability distribution of the parameters that quantify perceptual sensitivity change over time. We demonstrate the utility of the method in measuring the time course of dark adaptation. Simulations showed that the accuracy and precision of the estimated dark adaptation curve after one qCD run (root mean squared error (RMSE): 0.002; the half width of the 68.2% credible interval (HWCI): 0.016; standard deviation (SD): 0.020; all in log₁₀ units) was higher than those obtained by ten runs of the quick Forced-Choice (qFC) procedure (RMSE: 0.020; HWCI: 0.032; SD: 0.031) and ten runs of a weighted up-down staircase procedure (RMSE: 0.026; SD: 0.031). Further, the dark adaptation curve obtained from one qCD run in a psychophysics experiment was highly consistent with the average of four qFC runs (RMSE = 0.076 log₁₀ units). Overall, qCD provides a procedure to characterize the detailed time course of perceptual sensitivity change in both basic research and clinical applications.

Feasibility of interleaved Bayesian adaptive procedures in estimating the equal-loudness contour

A Bayesian adaptive procedure, the interleaved-equal-loudness contour (IELC) procedure, was developed to improve the efficiency in estimating the equal-loudness contour. Experiment 1 evaluated the test-retest reliability of the IELC procedure using six naive normal-hearing listeners. Two IELC runs of 200 trials were conducted and excellent test-retest reliability was found at both the group and individual levels. Using the same group of listeners, Experiment 2 compared the IELC procedure to two other procedures that required frequency-by-frequency testing. One of these procedures was the commonly adopted interleaved staircase (ISC) procedure from Jesteadt [(1980). Atten. Percept. Psychophys. 28, 85-88]. The other procedure, the interleaved maximum-likelihood (IML) procedure, was a modification of the updated maximum-likelihood procedure [Shen and Richards (2012). J. Acoust. Soc. Am. 132, 957-967]. For each of the ISC and IML procedures, two runs of approximately 500 trials were conducted, followed by one additional IELC run. The test-retest reliability of the IELC procedure was comparable to that of the ISC and IML procedure. The accuracies of all three procedures measured in Experiment 2 were similar, which was superior to the accuracies of the IELC runs from Experiment 1, indicating a potential training effect.

Psychometric function estimation by probabilistic classification

Conventional psychometric function (PF) estimation involves fitting a parametric, unidimensional sigmoid to binary subject responses, which is not readily extendible to higher order PFs. This study presents a nonparametric, Bayesian, multidimensional PF estimator that also relies upon traditional binary subject responses. This technique is built upon probabilistic classification (PC), which attempts to ascertain the subdomains corresponding to each subject response as a function of multiple independent variables. Increased uncertainty in the location of class boundaries results in a greater spread in the PF estimate, which is similar to a parametric PF estimate with a lower slope. PC was evaluated on both one-dimensional (1D) and two-dimensional (2D) simulated auditory PFs across a variety of function shapes and sample numbers. In the 1D case, PC demonstrated equivalent performance to conventional maximum likelihood regression for the same number of simulated responses. In the 2D case, where the responses were distributed across two independent variables, PC accuracy closely matched the accuracy of 1D maximum likelihood estimation at discrete values of the second variable. The flexibility and scalability of the PC formulation make this an excellent option for estimating traditional PFs as well as more complex PFs, which have traditionally lacked rigorous estimation procedures.

Fast, Continuous Audiogram Estimation Using Machine Learning

OBJECTIVES

Pure-tone audiometry has been a staple of hearing assessments for decades. Many different procedures have been proposed for measuring thresholds with pure tones by systematically manipulating intensity one frequency at a time until a discrete threshold function is determined. The authors have developed a novel nonparametric approach for estimating a continuous threshold audiogram using Bayesian estimation and machine learning classification. The objective of this study was to assess the accuracy and reliability of this new method relative to a commonly used threshold measurement technique.

DESIGN

The authors performed air conduction pure-tone audiometry on 21 participants between the ages of 18 and 90 years with varying degrees of hearing ability. Two repetitions of automated machine learning audiogram estimation and one repetition of conventional modified Hughson-Westlake ascending-descending audiogram estimation were acquired by an audiologist. The estimated hearing thresholds of these two techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz).

RESULTS

The two threshold estimate methods delivered very similar estimates at standard audiogram frequencies. Specifically, the mean absolute difference between estimates was 4.16 ± 3.76 dB HL. The mean absolute difference between repeated measurements of the new machine learning procedure was 4.51 ± 4.45 dB HL. These values compare favorably with those of other threshold audiogram estimation procedures. Furthermore, the machine learning method generated threshold estimates from significantly fewer samples than the modified Hughson-Westlake procedure while returning a continuous threshold estimate as a function of frequency.

CONCLUSIONS

The new machine learning audiogram estimation technique produces continuous threshold audiogram estimates accurately, reliably, and efficiently, making it a strong candidate for widespread application in clinical and research audiometry.