Nicholas R. Clark

Bio: Nicholas R. Clark is an academic researcher from University of Essex. The author has contributed to research in topics: Hearing aid & Noise. The author has an hindex of 5, co-authored 10 publications receiving 143 citations. Previous affiliations of Nicholas R. Clark include University of Nottingham.

Understanding Pitch Perception as a Hierarchical Process with Top-Down Modulation

Abstract: Pitch is one of the most important features of natural sounds, underlying the perception of melody in music and prosody in speech. However, the temporal dynamics of pitch processing are still poorly understood. Previous studies suggest that the auditory system uses a wide range of time scales to integrate pitch-related information and that the effective integration time is both task- and stimulus-dependent. None of the existing models of pitch processing can account for such task- and stimulus-dependent variations in processing time scales. This study presents an idealized neurocomputational model, which provides a unified account of the multiple time scales observed in pitch perception. The model is evaluated using a range of perceptual studies, which have not previously been accounted for by a single model, and new results from a neurophysiological experiment. In contrast to other approaches, the current model contains a hierarchy of integration stages and uses feedback to adapt the effective time scales of processing at each stage in response to changes in the input stimulus. The model has features in common with a hierarchical generative process and suggests a key role for efferent connections from central to sub-cortical areas in controlling the temporal dynamics of pitch processing.

A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise

Abstract: The potential contribution of the peripheral auditory efferent system to our understanding of speech in a background of competing noise was studied using a computer model of the auditory periphery and assessed using an automatic speech recognition system. A previous study had shown that a fixed efferent attenuation applied to all channels of a multi-channel model could improve the recognition of connected digit triplets in noise [G. J. Brown, R. T. Ferry, and R. Meddis, J. Acoust. Soc. Am. 127, 943-954 (2010)]. In the current study an anatomically justified feedback loop was used to automatically regulate separate attenuation values for each auditory channel. This arrangement resulted in a further enhancement of speech recognition over fixed-attenuation conditions. Comparisons between multi-talker babble and pink noise interference conditions suggest that the benefit originates from the model's ability to modify the amount of suppression in each channel separately according to the spectral shape of the interfering sounds.

A computer model of the auditory periphery and its application to the study of hearing.

Abstract: Computer models of the auditory periphery provide a tool for ­formulating theories concerning the relationship between the physiology of the auditory system and the perception of sounds both in normal and impaired hearing. However, the time-consuming nature of their construction constitutes a major impediment to their use, and it is important that transparent models be available on an ‘off-the-shelf’ basis to researchers. The MATLAB Auditory Periphery (MAP) model aims to meet these requirements and be freely available. The model can be used to simulate simple psychophysical tasks such as absolute threshold, pitch matching and forward masking and those used to measure compression and frequency selectivity. It can be used as a front end to automatic speech recognisers for the study of speech in quiet and in noise. The model can also simulate theories of hearing impairment and be used to make predictions about the efficacy of hearing aids. The use of the software will be described along with illustrations of its application in the study of the psychology of hearing.

Exploration of a physiologically-inspired hearing-aid algorithm using a computer model mimicking impaired hearing

Abstract: Objective: To use a computer model of impaired hearing to explore the effects of a physiologically-inspired hearing-aid algorithm on a range of psychoacoustic measures. Design: A computer model of ...

Analysis of compressive properties of the BioAid hearing aid algorithm

Abstract: Objective: This technical paper describes a biologically inspired hearing aid algorithm based on a computer model of the peripheral auditory system simulating basilar membrane compression, reflexive efferent feedback and its resulting properties. Design: Two evaluations were conducted on the core part of the algorithm, which is an instantaneous compression sandwiched between the attenuation and envelope extraction processes of a relatively slow feedback compressor. Study sample: The algorithm’s input/output (I/O) function was analysed for different stationary (ambient) sound levels, and the algorithm’s response to transient sinusoidal tone complexes was analysed and contrasted to that of a reference dynamic compressor. Results: The algorithm’s emergent properties are: (1) the I/O function adapts to the average sound level such that processing is linear for levels close to the ambient sound level and (2) onsets of transient signals are marked across time and frequency. Conclusion: Adaptive linearis...

The Organization of the Cochlear Receptor

Abstract: The yield of alkenols and cycloalkenols is substantially improved by carrying out the reaction of olefins with formaldehyde in the presence of selected catalysts. In accordance with one embodiment, alk-3-en-1-ols are produced in good yields from isobutylene and formaldehyde in the presence of organic carboxylic acid salts of Group IB metals.

Auditory-Inspired Speech Envelope Extraction Methods for Improved EEG-Based Auditory Attention Detection in a Cocktail Party Scenario

Abstract: This paper considers the auditory attention detection (AAD) paradigm, where the goal is to determine which of two simultaneous speakers a person is attending to The paradigm relies on recordings of the listener’s brain activity, eg, from electroencephalography (EEG) To perform AAD, decoded EEG signals are typically correlated with the temporal envelopes of the speech signals of the separate speakers In this paper, we study how the inclusion of various degrees of auditory modelling in this speech envelope extraction process affects the AAD performance, where the best performance is found for an auditory-inspired linear filter bank followed by power law compression These two modelling stages are computationally cheap, which is important for implementation in wearable devices, such as future neuro-steered auditory prostheses We also introduce a more natural way to combine recordings (over trials and subjects) to train the decoder, which reduces the dependence of the algorithm on regularization parameters Finally, we investigate the simultaneous design of the EEG decoder and the audio subband envelope recombination weights vector using either a norm-constrained least squares or a canonical correlation analysis, but conclude that this increases computational complexity without improving AAD performance

A Neurocomputational Model of the Mismatch Negativity

Abstract: The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering – a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN – in terms of latency and amplitude – and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and – more generally – illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.

Dysfunction of the auditory thalamus in developmental dyslexia.

Abstract: Developmental dyslexia, a severe and persistent reading and spelling impairment, is characterized by difficulties in processing speech sounds (i.e., phonemes). Here, we test the hypothesis that these phonological difficulties are associated with a dysfunction of the auditory sensory thalamus, the medial geniculate body (MGB). By using functional MRI, we found that, in dyslexic adults, the MGB responded abnormally when the task required attending to phonemes compared with other speech features. No other structure in the auditory pathway showed distinct functional neural patterns between the two tasks for dyslexic and control participants. Furthermore, MGB activity correlated with dyslexia diagnostic scores, indicating that the task modulation of the MGB is critical for performance in dyslexics. These results suggest that deficits in dyslexia are associated with a failure of the neural mechanism that dynamically tunes MGB according to predictions from cortical areas to optimize speech processing. This view on task-related MGB dysfunction in dyslexics has the potential to reconcile influential theories of dyslexia within a predictive coding framework of brain function.