Functional modelling of interaural time difference discrimination in acoustical and electrical hearing

OBJECTIVE: Interaural time differences (ITDs) are important for sound source localisation. We present a model to predict the just noticeable differences (JNDs) in ITD discrimination for normal hearing and electric stimulation through a cochlear implant. APPROACH: We combined periphery models of acoustic and electric stimulation with a novel JND in the ITD estimation stage, which consists of a shuffled cross correlogram and a binary classifier characterisation method. Furthermore, an evaluation framework is presented based on a large behavioural dataset. MAIN RESULTS: The model correctly predicts behavioural observations for unmodulated stimuli (such as pure tones and electric pulse trains) and modulated stimuli for modulation frequencies below 30 Hz. For higher modulation frequencies, the model predicts the observed behavioural trends, but tends to estimate higher ITD sensitivity. SIGNIFICANCE: The presented model can be used to investigate the implications of modifying the stimulus waveform on ITD sensitivity, and as such be applied in investigating sound encoding strategies.journal_title: Journal of Neural Engineering article_type: paper article_title: Functional modelling of interaural time difference discrimination in acoustical and electrical hearing copyright_information: © 2017 IOP Publishing Ltd date_received: 2016-09-20 date_accepted: 2017-05-02 date_epub: 2017-06-13status: publishe

Predicting Phoneme and Word Recognition using a Computational Model in Normal-Hearing Listeners

Behavioural and psychophysical measurements in audiology are currently a challenging and resource consuming task. Computational models are complementary to this classical approach, since they present several advantages, such as reproducibility, scalability, easily parametrization, and ease of use. The purpose of this work was to predict the scores of behavioural measurements using the model proposed by Zilany and Bruce (2006). First, phoneme and word scores were obtained from 20 normal-hearing adults using the Lilliput speech material (378 CVC words organised in 20 lists) under five different signal-to-noise (SNR) conditions. Scores were averaged across subjects. Then, for each SNR condition, a clean version and the noisy version of the stimulus were input to the model, which yielded their corresponding auditory-nerve response in the form of a neurogram. The neurogram similarity index measure (NSIM) was used to quantify the resemblance between them. Analysis of the behavioural scores showed that vowels were easier to identify than consonants, having a speech recognition threshold of -12.2 and -9.6 dB, respectively. Furthermore, regarding the computational model, preliminary results showed significant moderate to strong correlations ranging from 0.41 to 0.75 (median is 0.545, p < 0.05) between the NSIM metric and behavioural scores at a word level across lists. Future work will be focused in further simulations at the phoneme level using Zilany and Bruce’s model, as well as in investigating its sensitivity to phoneme transitions.status: publishe

Functional modelling of just noticeable differences in interaural time difference discrimination for bimodal and bilateral cochlear implant stimulation

status: publishe

Model-based Analysis of ITD Perception in Normal & Hearing Impaired Listeners

Interaural time differences (ITDs) are a fundamental cue for sound localization. Sensorineural hearing impaired (HI) listeners frequently have worsened localization and lateralization performance compared to normal hearing (NH) listeners, also reflected in deficient detection and discrimination of ITDs. It has been suggested that ITDs are processed by specialized cells that receive input from the auditory nerve (AN) from both ears and function as coincidence detectors. In this work, we developed a physiologically motivated model framework to evaluate temporal coding of ITDs at the periphery level of NH and HI listeners. AN responses to acoustic stimuli from both ears (in the form of spikes) with introduced ITDs (including a reference condition with ITD = 0 μs) were simulated using the phenomenological model proposed by Zilany et al. (2009). Next, we utilized shuffled cross-correlograms (SCCs, Joris, et al., 2006) to quantify the encoded ITD across the AN of both channels. Assuming that the auditory system favors the perception of smaller ITDs, we corrected the SCC curves using the weighting function proposed by Shackleton et al. (1992). Then, we predicted the imposed ITD by choosing the global maximum. As a decision variable, the distributions of the predicted reference and imposed ITDs were processed to obtain a receiver operating characteristic (ROC) in a 2 alternative force choice (AFC) procedure. This allowed us to compute the ITD just noticeable difference (JND) as the 75 % point of the psychometric curve. Finally, we evaluated the model by comparing the simulated ITD JNDs against literature behavioural data using bandpass noise with a center frequency and a bandwidth of 500 and 100 Hz, respectively. For the HI case, we used the same framework, but modified the AN model to account for different degrees of inner/outer haircell (IHC/OHC) impairment based on audiogram information. The proposed framework successfully predicted bandpass noise ITD JNDs of NH listeners. In the case of HI listeners, the model was able to account for trends in the data, although the high variability of participants' performance make the comparison to data difficult. These results provide the basis for a model-based quantification of ITD coding in NH and HI listeners at the periphery level. Future work will be focused in using the current approach to predict ITD discrimination performance in listeners with acoustic and electric hearing to optimize the representation of spatial information in hearing devices signal processing.status: publishe

Functional modelling of interaural time difference discrimination in acoustical and electrical hearing

status: publishe

Decision Device Comparison for Model-based Analysis of ITD Perception in Normal Hearing Listeners

Interaural time differences (ITDs) play an important role in sound localization and speech understanding in noise. Previously (Moncada-Torres et al., 2016), we developed a framework capable of predicting ITD just noticeable differences (JNDs) based on a physiological model of the auditory nerve (AN). However, the decision device employed there did not take into account the variability of the used data in a straightforward manner. In this work, we predicted ITD JNDs using two different decision devices in normal hearing listeners using information at the AN level. AN responses to acoustic stimuli from both ears (in the form of spikes) with and without introduced ITDs were simulated using the phenomenological model proposed by Zilany et al. (2009). Next, we used the shuffled cross-correlogram analysis (SCCs, Joris, et al., 2006) to quantify ITD encoding across the AN of both channels. Assuming that the auditory system is more sensitive to smaller ITDs, we corrected the SCC curves using the weighting function proposed by Stern and Shear (1996). Then, we predicted the imposed ITD by choosing the global maximum of the corrected curves. The distributions of the predicted reference and imposed ITDs were fed to two different decision modules: the receiver operating characteristic (ROC) and the detection index (d'). These allowed us to calculate the ITD JND as the 79.4% and 1.5 point, respectively, of the neurometric curve. Finally, we evaluated the performance of the decision devices' predictions by comparing them against literature behavioural data using pure tones with frequencies from 250 to 1400 Hz. The proposed framework showed similar trends as in psychoacoustical data, with the d' metric being higher correlated with it. Future work will be focused in using the framework’s improved pipeline to predict ITD discrimination performance in hearing impaired listeners and well as in optimizing hearing aids/cochlear implants signal processing.status: publishe

Speech Envelope Enhancement Instantaneously Effaces Atypical Speech Perception in Dyslexia.

OBJECTIVES: Increasing evidence exists that poor speech perception abilities precede the phonological deficits typically observed in dyslexia, a developmental disorder in learning to read. Impaired processing of dynamic features of speech, such as slow amplitude fluctuations and transient acoustic cues, disrupts effortless tracking of the speech envelope and constrains the development of adequate phonological skills. In this study, a speech envelope enhancement (EE) strategy was implemented to reduce speech perception deficits by students with dyslexia. The EE emphasizes onset cues and reinforces the temporal structure of the speech envelope specifically. DESIGN: Speech perception was assessed in 42 students with and without dyslexia using a sentence repetition task in a speech-weighted background noise. Both natural and vocoded speech were used to assess the contribution of the temporal envelope on the speech perception deficit. Their envelope-enhanced counterparts were added to each baseline condition to administer the effect of the EE algorithm. In addition to speech-in-noise perception, general cognitive abilities were assessed. RESULTS: Results demonstrated that students with dyslexia not only benefit from EE but benefit more from it than typical readers. Hence, EE completely normalized speech reception thresholds for students with dyslexia under adverse listening conditions. In addition, a correlation between speech perception deficits and phonological processing was found for students with dyslexia, further supporting the relation between speech perception abilities and reading skills. Similar results and relations were found for conditions with natural and vocoded speech, providing evidence that speech perception deficits in dyslexia stem from difficulties in processing the temporal envelope. CONCLUSIONS: Using speech EE, speech perception skills in students with dyslexia were improved passively and instantaneously, without requiring any explicit learning. In addition, the observed positive relationship between speech processing and advanced phonological skills opens new avenues for specific intervention strategies that directly target the potential core deficit in dyslexia.status: publishe

Activity classification based on inertial and barometric pressure sensors at different anatomical locations

Miniature, wearable sensor modules are a promising technology to monitor activities of daily living (ADL) over extended periods of time. To assure both user compliance and meaningful results, the selection and placement site of sensors requires careful consideration. We investigated these aspects for the classification of 16 ADL in 6 healthy subjects under laboratory conditions using ReSense, our custom-made inertial measurement unit enhanced with a barometric pressure sensor used to capture activity-related altitude changes. Subjects wore a module on each wrist and ankle, and one on the trunk. Activities comprised whole body movements as well as gross and dextrous upper-limb activities. Wrist-module data outperformed the other locations for the three activity groups. Specifically, overall classification accuracy rates of almost 93% and more than 95% were achieved for the repeated holdout and user-specific validation methods, respectively, for all 16 activities. Including the altitude profile resulted in a considerable improvement of up to 20% in the classification accuracy for stair ascent and descent. The gyroscopes provided no useful information for activity classification under this scheme. The proposed sensor setting could allow for robust long-term activity monitoring with high compliance in different patient populations.ISSN:0967-3334ISSN:1361-657

Predicting phoneme and word recognition in noise using a computational model of the auditory periphery

Several filterbank-based metrics have been proposed to predict speech intelligibility (SI). However, these metrics incorporate little knowledge of the auditory periphery. Neurogram-based metrics provide an alternative, incorporating knowledge of the physiology of hearing by using a mathematical model of the auditory nerve response. In this work, SI was assessed utilizing different filterbank-based metrics (the speech intelligibility index and the speech-based envelope power spectrum model) and neurogram-based metrics, using the biologically inspired model of the auditory nerve proposed by Zilany, Bruce, Nelson, and Carney [(2009), J. Acoust. Soc. Am. 126(5), 2390–2412] as a front-end and the neurogram similarity metric and spectro temporal modulation index as a back-end. Then, the correlations with behavioural scores were computed. Results showed that neurogram-based metrics representing the speech envelope showed higher correlations with the behavioural scores at a word level. At a per-phoneme level, it was found that phoneme transitions contribute to higher correlations between objective measures that use speech envelope information at the auditory periphery level and behavioural data. The presented framework could function as a useful tool for the validation and tuning of speech materials, as well as a benchmark for the development of speech processing algorithms.status: publishe

VANTAGE6:an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange

Answering many of the research questions in the field of cancer informatics requires incorporating and centralizing data that are hosted by different parties. Federated Learning (FL) has emerged as a new approach in which a global model can be generated without disclosing private patient data by keeping them at their original location. Flexible, user-friendly, and robust infrastructures are crucial for bringing FL solutions to the day-to-day work of the cancer epidemiologist. In this paper, we present an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange, VANTAGE6. We provide a detailed description of its conceptual design, modular architecture, and components. We also show a few examples where VANTAGE6 has been successfully used in research on observational cancer data. Developing and deploying technology to support federated analyses - such as VANTAGE6 - will pave the way for the adoption and mainstream practice of this new approach for analyzing decentralized data.</p