Analysis of compressive properties of the BioAid hearing aid algorithm.

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 linearisation and onset marking, as inherent compressive features of the algorithm, provide potentially beneficial features to hearing-impaired listeners with a relatively simple circuit. The algorithm offers a new, biological perspective on hearing aid amplification.

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

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 a hypothetical impaired listener's hearing was constructed by adjusting parameters of a computer model of normal hearing. Absolute thresholds, estimates of compression, and frequency selectivity (summarized to a hearing profile) were assessed using this model with and without pre-processing the stimuli by a hearing-aid algorithm. The influence of different settings of the algorithm on the impaired profile was investigated. To validate the model predictions, the effect of the algorithm on hearing profiles of human impaired listeners was measured. STUDY SAMPLE: A computer model simulating impaired hearing (total absence of basilar membrane compression) was used, and three hearing-impaired listeners participated. RESULTS: The hearing profiles of the model and the listeners showed substantial changes when the test stimuli were pre-processed by the hearing-aid algorithm. These changes consisted of lower absolute thresholds, steeper temporal masking curves, and sharper psychophysical tuning curves. CONCLUSION: The hearing-aid algorithm affected the impaired hearing profile of the model to approximate a normal hearing profile. Qualitatively similar results were found with the impaired listeners' hearing profiles.

The robustness of speech representations obtained from simulated auditory nerve fibers under different noise conditions.

Different methods of extracting speech features from an auditory model were systematically investigated in terms of their robustness to different noises. The methods either computed the average firing rate within frequency channels (spectral features) or inter-spike-intervals (timing features) from the simulated auditory nerve response. When used as the front-end for an automatic speech recognizer, timing features outperformed spectral features in Gaussian noise. However, this advantage was lost in babble, because timing features extracted the spectro-temporal structure of babble noise, which is similar to the target speaker. This suggests that different feature extraction methods are optimal depending on the background noise.

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

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.

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

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.

Understanding pitch perception as a hierarchical process with top-down modulation.

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.