29,294 research outputs found
A point process framework for modeling electrical stimulation of the auditory nerve
Model-based studies of auditory nerve responses to electrical stimulation can
provide insight into the functioning of cochlear implants. Ideally, these
studies can identify limitations in sound processing strategies and lead to
improved methods for providing sound information to cochlear implant users. To
accomplish this, models must accurately describe auditory nerve spiking while
avoiding excessive complexity that would preclude large-scale simulations of
populations of auditory nerve fibers and obscure insight into the mechanisms
that influence neural encoding of sound information. In this spirit, we develop
a point process model of the auditory nerve that provides a compact and
accurate description of neural responses to electric stimulation. Inspired by
the framework of generalized linear models, the proposed model consists of a
cascade of linear and nonlinear stages. We show how each of these stages can be
associated with biophysical mechanisms and related to models of neuronal
dynamics. Moreover, we derive a semi-analytical procedure that uniquely
determines each parameter in the model on the basis of fundamental statistics
from recordings of single fiber responses to electric stimulation, including
threshold, relative spread, jitter, and chronaxie. The model also accounts for
refractory and summation effects that influence the responses of auditory nerve
fibers to high pulse rate stimulation. Throughout, we compare model predictions
to published physiological data and explain differences in auditory nerve
responses to high and low pulse rate stimulation. We close by performing an
ideal observer analysis of simulated spike trains in response to sinusoidally
amplitude modulated stimuli and find that carrier pulse rate does not affect
modulation detection thresholds.Comment: 1 title page, 27 manuscript pages, 14 figures, 1 table, 1 appendi
A binaural grouping model for predicting speech intelligibility in multitalker environments
Spatially separating speech maskers from target speech often leads to a large intelligibility improvement. Modeling this phenomenon has long been of interest to binaural-hearing researchers for uncovering brain mechanisms and for improving signal-processing algorithms in hearing-assistive devices. Much of the previous binaural modeling work focused on the unmasking enabled by binaural cues at the periphery, and little quantitative modeling has been directed toward the grouping or source-separation benefits of binaural processing. In this article, we propose a binaural model that focuses on grouping, specifically on the selection of time-frequency units that are dominated by signals from the direction of the target. The proposed model uses Equalization-Cancellation (EC) processing with a binary decision rule to estimate a time-frequency binary mask. EC processing is carried out to cancel the target signal and the energy change between the EC input and output is used as a feature that reflects target dominance in each time-frequency unit. The processing in the proposed model requires little computational resources and is straightforward to implement. In combination with the Coherence-based Speech Intelligibility Index, the model is applied to predict the speech intelligibility data measured by Marrone et al. The predicted speech reception threshold matches the pattern of the measured data well, even though the predicted intelligibility improvements relative to the colocated condition are larger than some of the measured data, which may reflect the lack of internal noise in this initial version of the model.R01 DC000100 - NIDCD NIH HH
Frame Theory for Signal Processing in Psychoacoustics
This review chapter aims to strengthen the link between frame theory and
signal processing tasks in psychoacoustics. On the one side, the basic concepts
of frame theory are presented and some proofs are provided to explain those
concepts in some detail. The goal is to reveal to hearing scientists how this
mathematical theory could be relevant for their research. In particular, we
focus on frame theory in a filter bank approach, which is probably the most
relevant view-point for audio signal processing. On the other side, basic
psychoacoustic concepts are presented to stimulate mathematicians to apply
their knowledge in this field
Real-time motor rotation frequency detection with event-based visual and spike-based auditory AER sensory integration for FPGA
Multisensory integration is commonly
used in various robotic areas to collect more
environmental information using different and
complementary types of sensors. Neuromorphic
engineers mimics biological systems behavior to
improve systems performance in solving engineering
problems with low power consumption. This work
presents a neuromorphic sensory integration scenario
for measuring the rotation frequency of a motor using
an AER DVS128 retina chip (Dynamic Vision Sensor)
and a stereo auditory system on a FPGA completely
event-based. Both of them transmit information with
Address-Event-Representation (AER). This
integration system uses a new AER monitor hardware
interface, based on a Spartan-6 FPGA that allows two
operational modes: real-time (up to 5 Mevps through
USB2.0) and data logger mode (up to 20Mevps for
33.5Mev stored in onboard DDR RAM). The sensory
integration allows reducing prediction error of the
rotation speed of the motor since audio processing
offers a concrete range of rpm, while DVS can be
much more accurate.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0
Individual differences in auditory brainstem response wave characteristics : relations to different aspects of peripheral hearing loss
Little is known about how outer hair cell loss interacts with noise-induced and age-related auditory nerve degradation (i.e., cochlear synaptopathy) to affect auditory brainstem response (ABR) wave characteristics. Given that listeners with impaired audiograms likely suffer from mixtures of these hearing deficits and that ABR amplitudes have successfully been used to isolate synaptopathy in listeners with normal audiograms, an improved understanding of how different hearing pathologies affect the ABR source generators will improve their sensitivity in hearing diagnostics. We employed a functional model for human ABRs in which different combinations of hearing deficits were simulated and show that highfrequency cochlear gain loss steepens the slope of the ABRWave-V latency versus intensity and amplitude versus intensity curves. We propose that grouping listeners according to a ratio of these slope metrics (i.e., the ABR growth ratio) might offer a way to factor out the outer hair cell loss deficit and maximally relate individual differences for constant ratios to other peripheral hearing deficits such as cochlear synaptopathy. We compared the model predictions to recorded click-ABRs from 30 participants with normal or high-frequency sloping audiograms and confirm the predicted relationship between the ABR latency growth curve and audiogram slope. Experimental ABR amplitude growth showed large individual differences and was compared with the Wave-I amplitude, Wave-V/I ratio, or the interwaveI-W latency in the same listeners. The model simulations along with the ABR recordings suggest that a hearing loss profile depicting the ABR growth ratio versus the Wave-I amplitude or Wave-V/I ratio might be able to differentiate outer hair cell deficits from cochlear synaptopathy in listeners with mixed pathologies
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source
localisation in an urban scenario. Specifically, we are interested in spotting
the presence of horns, and sirens of emergency vehicles. In order to obtain a
reliable system able to operate robustly despite the presence of traffic noise,
which can be copious, unstructured and unpredictable, we propose to treat the
spectrograms of incoming stereo signals as images, and apply semantic
segmentation, based on a Unet architecture, to extract the target sound from
the background noise. In a multi-task learning scheme, together with signal
denoising, we perform acoustic event classification to identify the nature of
the alerting sound. Lastly, we use the denoised signals to localise the
acoustic source on the horizon plane, by regressing the direction of arrival of
the sound through a CNN architecture. Our experimental evaluation shows an
average classification rate of 94%, and a median absolute error on the
localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of
2.5{\deg} when operating on frames of 2.5s. The system offers excellent
performance in particularly challenging scenarios, where the noise level is
remarkably high.Comment: 6 pages, 9 figure
Applicability of subcortical EEG metrics of synaptopathy to older listeners with impaired audiograms
Emerging evidence suggests that cochlear synaptopathy is a common feature of sensorineural hearing loss, but it is not known to what extent electrophysiological metrics targeting synaptopathy in animals can be applied to people, such as those with impaired audiograms. This study investigates the applicability of subcortical electrophysiological measures associated with synaptopathy, i.e., auditory brainstem responses (ABRs) and envelope following responses (EFRs), to older participants with high-frequency sloping audiograms. The outcomes of this study are important for the development of reliable and sensitive synaptopathy diagnostics in people with normal or impaired outer-hair-cell function. Click-ABRs at different sound pressure levels and EFRs to amplitude-modulated stimuli were recorded, as well as relative EFR and ABR metrics which reduce the influence of individual factors such as head size and noise floor level on the measures. Most tested metrics showed significant differences between the groups and did not always follow the trends expected from synaptopathy. Age was not a reliable predictor for the electrophysiological metrics in the older hearing-impaired group or young normal-hearing control group. This study contributes to a better understanding of how electrophysiological synaptopathy metrics differ in ears with healthy and impaired audiograms, which is an important first step towards unravelling the perceptual consequences of synaptopathy.(C) 2019 Elsevier B.V. All rights reserved
The Neural Particle Filter
The robust estimation of dynamically changing features, such as the position
of prey, is one of the hallmarks of perception. On an abstract, algorithmic
level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing
signals based on the history of observations, provides a mathematical framework
for dynamic perception in real time. Since the general, nonlinear filtering
problem is analytically intractable, particle filters are considered among the
most powerful approaches to approximating the solution numerically. Yet, these
algorithms prevalently rely on importance weights, and thus it remains an
unresolved question how the brain could implement such an inference strategy
with a neuronal population. Here, we propose the Neural Particle Filter (NPF),
a weight-less particle filter that can be interpreted as the neuronal dynamics
of a recurrently connected neural network that receives feed-forward input from
sensory neurons and represents the posterior probability distribution in terms
of samples. Specifically, this algorithm bridges the gap between the
computational task of online state estimation and an implementation that allows
networks of neurons in the brain to perform nonlinear Bayesian filtering. The
model captures not only the properties of temporal and multisensory integration
according to Bayesian statistics, but also allows online learning with a
maximum likelihood approach. With an example from multisensory integration, we
demonstrate that the numerical performance of the model is adequate to account
for both filtering and identification problems. Due to the weightless approach,
our algorithm alleviates the 'curse of dimensionality' and thus outperforms
conventional, weighted particle filters in higher dimensions for a limited
number of particles
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