An Auditory Model Based Strategy for Cochlear Implants (original) (raw)
Related papers
IEEE Transactions on Biomedical Circuits and Systems, 2013
Mimicking the human ear on the basis of auditory models has become a viable approach in many applications by now. However, only a few attempts have been made to extend the scope of physiological ear models to be employed in cochlear implants (CI). Contemporary CI systems rely on much simpler filter banks and simulate the natural signal processing of a healthy cochlea to only a very limited extent. When looking at rehabilitation outcomes, current systems seem to have reached their peak potential, which signals the need for better algorithms and/or technologies. In this paper, we present a novel sound processing strategy, SAM (Stimulation based on Auditory Modeling), that is based on neurophysiological models of the human ear and can be employed in auditory prostheses. It incorporates active cochlear filtering (basilar membrane and outer hair cells) along with the mechanoelectrical transduction of the inner hair cells, so that several psychoacoustic phenomena are accounted for inherently. Although possible, current implementation does not make use of parallel stimulation of the electrodes, which matches state-of-the-art CI hardware. This paper elaborates on SAM's signal processing and provides a computational evaluation of the strategy. Results show that aspects of normal cochlear processing that are missing in common strategies can be replicated by SAM. This is supposed to improve overall CI user performance, which we have at least partly proven in a pilot study with implantees.
A Novel Speech Processing Applications in Cochlear Implant Research
Journal of Medical Imaging and Health Informatics, 2015
In this paper we present the conception and the implementation of a speech processing interface for cochlea prosthesis. This module is based on a numerical speech processing algorithm which models the infected ear and generates the stimulus signals for the cilia cells (brain). This interface uses a Gammachirp filterbank based on IIR filters. The central frequencies and ERB bands are computed with Glasberg and Moore models. To validate our work, we tested it on several words pronounced by different speakers. The results show that the new model called MODCC (Modified Cepstral Coefficients) gives results that are comparable to MFCC (Mel Frequency Cepstral Coefficients) features, GC-PLP (GammaChirp Perceptual Linear Prediction) and GC-Cepst (GammaChirp Cepstral). Experimental results show the best performance of this architecture.
2013
Stimulation based on auditory modeling, or SAM, is a new speechprocessing strategy for cochlear implants that we developed recently at Fraunhofer IDMT. SAM incorporates active cochlear filtering along with the mechanoelectrical transduction of the inner hair cells, so that several psychoacoustic phenomena are accounted for inherently. SAM was tested with a group of five CI users: We investigated speech perception in quiet and in the presence of noise or reverberation, pitch discrimination abilities (for pure tones and sung vowels), and consonant discrimination. We also asked for subjective quality rating for speech and music snippets. Tests were repeated with the everyday strategy of the implantees and results were compared. This paper presents the test results in detail and compares outcomes with those of the previously published simulation studies. Results are encouraging, although more tests would be needed to increase statistical significance.
Analysis of Speech Processing Strategies in Cochlear Implants
Journal of Computer Science, 2008
Cochlear implants can restore partial hearing to profoundly deaf people; the main function of these prostheses is to electrically stimulate the auditory nerve using an electrode array inserted in the cochlea. The acoustic signal is picked up by a microphone and analyzed. Then the extracted parameters of the signal are coded to generate electrical signals reconstituting the original signal. Currently all commercialized implants are multichannel they allow to stimulate the auditory nerve at different place of the cochlea, exploiting the tonotopic coding of the frequencies. This research will present an overview of various signal processing techniques that have been used for cochlear prosthesis over the years.
Valid Acoustic Models of Cochlear Implants: One Size Does Not Fit All
Otology & Neurotology, 2021
Hypothesis: This study tests the hypothesis that it is possible to find tone or noise vocoders that sound similar and result in similar speech perception scores to a cochlear implant (CI). This would validate the use of such vocoders as acoustic models of CIs. We further hypothesize that those valid acoustic models will require a personalized amount of frequency mismatch between input filters and output tones or noise bands. Background: Noise or tone vocoders have been used as acoustic models of CIs in hundreds of publications but never been convincingly validated.
Application of loudness models to sound processing for cochlear implants
The Journal of the Acoustical Society of America, 2003
A new paradigm for processing sound signals for multiple-electrode cochlear implants is introduced, and results are presented from an initial psychophysical evaluation of its effect on the perceived loudness of complex sounds. A real-time processing scheme based on this paradigm, called SpeL, has been developed primarily to improve control of loudness for implant users. SpeL differs from previous schemes in several ways. Most importantly, it incorporates a published numerical model which predicts the loudness perceived by implant users for complex patterns of pulsatile electric stimulation as a function of the pulses' physical parameters. This model is controlled by the output of a corresponding model that estimates the loudness perceived by normally hearing listeners for complex sounds. The latter model produces an estimate of the specific loudness arising from an acoustic signal. In SpeL, the specific loudness function, which describes the contribution to total loudness of each of a number of frequency bands Í‘or cochlear positionsÍ’, is converted to a pattern of electric stimulation on an appropriate set of electrodes. By application of the loudness model for electric stimulation, this pattern is designed to produce a specific loudness function for the implant user which approximates that produced by the normal-hearing model for the same input signal. The results of loudness magnitude estimation experiments with five users of the SpeL scheme confirmed that the psychophysical functions relating overall loudness perceived to input sound level for five complex acoustic signals were, on average, very similar to those for normal hearing.
The history and future of neural modeling for cochlear implants
Network (Bristol, England), 2016
This special issue of Network: Computation in Neural Systems on the topic of "Computational models of the electrically stimulated auditory system" incorporates review articles spanning a wide range of approaches to modeling cochlear implant stimulation of the auditory system. The purpose of this overview paper is to provide a historical context for the different modeling endeavors and to point toward how computational modeling could play a key role in the understanding, evaluation, and improvement of cochlear implants in the future.
Cochlear Implant (CI) is the technology, which provides solutions for different types of hearing loss. There is different challenges face by the designers of Cochlear implant in developing signal processing techniques so that can provide the voice which function like normal cochlea of inner ear. This paper discusses various signal processing and neural network techniques used for processing speech data in CI in chronological order of development. The paper also reviews the existing CI devices, their development and techniques used for speech processing in existing CI devices. Wavelet analysis, PET, fMRI are a few techniques used for feature extractions, ambient noise removal and spectral estimation. There are also various other techniques which we will be discussing here. We will also be discussing the development of some environment-specific noise suppression algorithms. Even though there have been some developments in the field of cochlear implant this is an area that is seeing rapid growth. This is the zone of our further discussion.
Technology and health care : official journal of the European Society for Engineering and Medicine, 2017
Speech synthesis models have been considered as viable tools for performance evaluation of cochlear stimulation algorithms, due to the difficulties of clinical tests. The present study has developed a tool that can be used before any audio signal reconstruction algorithm, which shows more conformity with the electrophysiological parameters of the patient in evaluation of the cochlear implant stimulation algorithms. In this method, excitable nerve fiber characteristics such as stimulation threshold and effective refractory period have been considered in the signal pre-reconstruction process. This algorithm subsumes the user's biological parameters (e.g., the manner of distribution of the remaining intact nerve fibers) as well as the stimulation signal parameters (e.g., stimulation rate, pulse width, amplitude of stimulation, the distance between stimulation electrode and fibers) in the signal pre-reconstruction. Effect of changes in these parameters can be observed by the number ...
A comparative study of seven human cochlear filter models
Auditory models have been developed for decades to simulate characteristics of the human auditory system, but it is often unknown how well auditory models compare to each other or perform in tasks they were not primarily designed for. This study systematically analyzes predictions of seven publicly-available cochlear filter models in response to a fixed set of stimuli to assess their capabilities of reproducing key aspects of human cochlear mechanics. The following features were assessed at frequencies of 0.5, 1, 2, 4, and 8 kHz: cochlear excitation patterns, nonlinear response growth, frequency selectivity, group delays, signal-in-noise processing, and amplitude modulation representation. For each task, the simulations were compared to available physiological data recorded in guinea pigs and gerbils as well as to human psychoacoustics data. The presented results provide application-oriented users with comprehensive information on the advantages, limitations and computation costs of these seven mainstream cochlear filter models.