ABSTRACT
Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.
ABSTRACT
To study the spatial hearing abilities of bilateral hearing-aid users in multi-talker situations, 20 subjects received fittings configured to preserve acoustic cues salient for spatial hearing. Following acclimatization, speech reception thresholds (SRTs) were measured for three competing talkers that were either co-located or spatially separated along the front-back or left-right dimension. In addition, the subjects' working memory and attentional abilities were measured. Left-right SRTs varied over more than 14 dB, while front-back SRTs varied over more than 8 dB. Furthermore, significant correlations were observed between left-right SRTs, age, and low-frequency hearing loss, and also between front-back SRTs, age, and high-frequency aided thresholds. Concerning cognitive effects, left-right performance was most strongly related to attentional abilities, while front-back performance showed a relation to working memory abilities. Altogether, these results suggest that, due to raised hearing thresholds and aging, hearing-aid users have reduced access to interaural and monaural spatial cues as well as a diminished ability to 'enhance' a target signal by means of top-down processing. These deficits, in turn, lead to impaired functioning in complex listening environments.
