Tobias Goehring

Tobias Goehring
University of Cambridge | Cam · MRC Cognition and Brain Sciences Unit

PhD, Dipl.-Ing.

About

36
Publications
6,329
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
387
Citations
Citations since 2016
34 Research Items
387 Citations
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
Introduction
Senior Research Scientist (MRC CDA Fellow) at the MRC Cognition and Brain Sciences Unit, University of Cambridge, UK, leading the Deep Hearing Lab (www.deephearinglab.com). Research in Hearing Science by combining techniques from Engineering, Auditory Neuroscience and Machine Learning with application to Medical Devices. My focus lies on improving the perception of speech for people with hearing loss in everyday life.
Additional affiliations
May 2017 - present
University of Cambridge
Position
  • Research Associate
February 2017 - April 2017
Macquarie University
Position
  • PostDoc Position
November 2013 - January 2017
University of Southampton
Position
  • Research Assistant
Education
September 2005 - November 2011
Technische Universität Braunschweig
Field of study
  • Electrical Engineering

Publications

Publications (36)
Article
Full-text available
Goal: Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprosthetics, accelerating innovation in the field. Here, we present an end-to-end computational model for predicting speech perception with cochlear implants (CI), the most widely-used neuroprosthetic. Methods: The...
Article
Full-text available
Millions of people around the world have difficulty hearing. Hearing aids and cochlear implants help people hear better, especially in quiet places. Unfortunately, these devices do not always help in noisy situations like busy classrooms or restaurants. This means that a person with hearing loss may struggle to follow a conversation with friends or...
Preprint
Objectives: Electrically-Evoked Compound Action-Potentials (ECAPs) can be recorded using the electrodes in a cochlear implant (CI) and represent the synchronous responses of the electrically-stimulated auditory-nerve. ECAPs can be obtained using a forward-masking method that measures the neural response to a probe and masker electrode separately an...
Article
Full-text available
Cochlear implants (CIs) are the world’s most successful sensory prosthesis and have been the subject of intense research and development in recent decades. We critically review the progress in CI research, and its success in improving patient outcomes, from the turn of the century to the present day. The review focuses on the processing, stimulatio...
Preprint
Cochlear implants (CIs) are the world’s most successful sensory prosthesis and have been the subject of intense research and development in recent decades. We critically review the progress in CI research, and its success in improving patient outcomes, from the turn of the century to the present day. The introduction of directional microphones and...
Article
Full-text available
Cochlear implants (CIs) are neuroprostheses that partially restore hearing for people with severe-to-profound hearing loss. While CIs can provide good speech perception in quiet listening situations for many, they fail to do so in environments with interfering sounds for most listeners. Previous research suggests that this is due to detrimental int...
Article
Full-text available
The knowledge of patient-specific neural excitation patterns from cochlear implants (CIs) can provide important information for optimizing efficacy and improving speech perception outcomes. The Panoramic ECAP ('PECAP') method (Cosentino et al. 2015) uses forward-masked electrically evoked compound action-potentials (ECAPs) to estimate neural activa...
Article
Full-text available
Cochlear implants use electrical stimulation of the auditory nerve to restore the sensation of hearing to deaf people. Unfortunately, the stimulation current spreads extensively within the cochlea, resulting in “blurring” of the signal, and hearing that is far from normal. Current spread can be indirectly measured using the implant electrodes for b...
Article
Full-text available
The STRIPES (Spectro-Temporal Ripple for Investigating Processor EffectivenesS) test is a psychophysical test of spectro-temporal resolution developed for cochlear-implant (CI) listeners. Previously, the test has been strictly controlled to minimize the introduction of extraneous, nonspectro-temporal cues. Here, the effect of relaxing many of those...
Preprint
The knowledge of patient-specific neural excitation patterns from cochlear implants can provide important information for optimising efficacy and improving speech perception outcomes. The Panoramic ECAP (or ‘PECAP’) method (Cosentino, et al., 2015) uses forward-masked electrically evoked compound action potentials (ECAPs) to estimate neural activat...
Article
Full-text available
Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and lead to difficulties with speech perception. Strategies that reduce channel interaction therefore have the potential to improve speech-in-noise perception by CI...
Article
Full-text available
Speech recognition in noisy environments remains a challenge for cochlear implant (CI) recipients. Unwanted charge interactions between current pulses, both within and between electrode channels, are likely to impair performance. Here we investigate the effect of reducing the number of current pulses on speech perception. This was achieved by imple...
Preprint
Full-text available
Speech recognition in noisy environments remains a challenge for cochlear implant (CI) recipients. Unwanted charge interactions between current pulses in the same and across different electrode channels are likely to impair performance. Here we investigate the effect of reducing the number of current pulses on speech perception. This was achieved b...
Preprint
Full-text available
The STRIPES (Spectro-Temporal Ripple for Investigating Processor EffectivenesS) test is a psychophysical test of spectro-temporal resolution developed for cochlear implant (CI) listeners. Previously, the test has been strictly controlled to minimize the introduction of extraneous, non-spectro-temporal cues. Here, the effect of relaxing many of thos...
Preprint
Full-text available
Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and lead to difficulties with speech perception. Strategies that reduce channel interaction therefore have the potential to improve speech-in-noise perception by CI...
Conference Paper
Full-text available
Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and reduce the effective number of channels a CI provides. Therefore, strategies to reduce channel interaction have the potential to improve speech-in-noise percepti...
Article
Full-text available
Cochlear implant (CI) users receive only limited sound information through their implant, which means that they struggle to understand speech in noisy environments. Recent work has suggested that combining the electrical signal from the CI with a haptic signal that provides crucial missing sound information (“electro-haptic stimulation”; EHS) could...
Article
Full-text available
Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise. Noise-reduction algorithms have produced benefits but relied on a priori information about the target speaker and/or background noise. A recurrent neural network (RNN) algorithm was developed for enhancing speech in n...
Article
Full-text available
Thresholds of asymmetric pulses presented to cochlear implant (CI) listeners depend on polarity in a way that differs across subjects and electrodes. It has been suggested that lower thresholds for cathodic-dominant compared to anodic-dominant pulses reflect good local neural health. We evaluated the hypothesis that this polarity effect (PE) can be...
Preprint
Full-text available
Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise such as competing talkers or traffic. Algorithms that facilitate speech perception by attenuating background noise have produced benefits but relied on a priori information about the target speaker and/or background no...
Preprint
Thresholds of asymmetric pulses presented to cochlear implant (CI) listeners depend on polarity in a way that differs across subjects and electrodes. It has been suggested that lower thresholds for cathodic-dominant compared to anodic-dominant pulses reflect good local neural survival. We evaluated the hypothesis that this polarity effect (PE) can...
Article
The effects on speech intelligibility and sound quality of two noise-reduction algorithms were compared: a deep recurrent neural network (RNN) and spectral subtraction (SS). The RNN was trained using sentences spoken by a large number of talkers with a variety of accents, presented in babble. Different talkers were used for testing. Participants wi...
Preprint
Cochlear implant (CI) users receive only limited sound information through their implant, which means that they struggle to understand speech in noisy environments. Recent work has suggested that combining the electrical signal from the CI with a haptic signal that provides crucial missing sound information (“electro-haptic stimulation”; EHS) could...
Article
Full-text available
Many cochlear implant (CI) users achieve excellent speech understanding in acoustically quiet conditions, but most perform poorly in the presence of background noise. An important contributor to this poor speech-in-noise performance is the limited transmission of low-frequency sound information through CIs. Recent work has suggested that tactile pr...
Article
Full-text available
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male...
Article
Full-text available
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male...
Article
Full-text available
Objective: Processing delay is one of the important factors that limit the development of novel algorithms for hearing devices. In this study, both normal-hearing listeners and listeners with hearing loss were tested for their tolerance of processing delay up to 50 ms using a real-time setup for own-voice and external-voice conditions based on lin...
Article
Full-text available
Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wiener filtering, was compared. Two algorithms based on neural networks were examined, one using a prev...
Article
Full-text available
Speech understanding in adverse acoustic environments is still a major problem for users of hearing-instruments. Recent studies on supervised speech segregation show good promise to alleviate this problem by separating speech-dominated from noise-dominated spectro-temporal regions with estimated time-frequency masks. The current study compared a pr...
Thesis
Hearing loss can lead to problems with communication, affect the psychological wellbeing and decrease the quality of life of an affected person. One of the main challenges for people with hearing loss is speech perception in noisy environments. Whereas hearing devices such as hearing aids and cochlear implants successfully provide high levels of sp...
Article
Full-text available
Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts...
Conference Paper
Full-text available
Speech understanding in adverse acoustic environments is still a major problem for users of hearinginstruments. Recent studies on supervised speech segregation show good promise to alleviate this problem by separating speech-dominated from noise-dominated spectro-temporal regions with estimated time-frequency masks. The current study compared a pre...
Poster
Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, especially in the presence of a fluctuating masker. Motivated by previous intelligibility studies of speech synthesized using the ideal binary mask, we propose a framework that integrates a m...
Conference Paper
We propose and introduce a testing method for subjective speech quality evaluation of hands-free telephony systems in car environments. In evaluating car hands-free speech quality it is desired to have a procedure available which is repeatable and reproducible, significant and convincing, yet fast and especially resource effective. Proposed is a me...

Network

Cited By