Multirate systems and filter banks
Multirate systems and filter banks
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Hybrid wavelet-support vector classification of waveforms
Journal of Computational and Applied Mathematics
Shiftable multiscale transforms
IEEE Transactions on Information Theory - Part 2
| Hi-index | 0.00 |
Objective: The objective of our research is to structure a foundation for an electrophysiological loudness scaling measurement, in particular to estimate an uncomfortable loudness (UCL) level by using the hybrid wavelet-kernel novelty detection (HWND). Methods and materials: Late auditory evoked potentials (LAEPs) were obtained from 20 normal hearing adults. These LAEPs were stimulated by 4 intensity levels (60 decibel (dB) sound pressure level (SPL), 70dB SPL, 80dB SPL, and 90dB SPL). We have extracted the habituation correlates in LAEPs by using HWND. For this, we employed a lattice structure-based wavelet frame decompositions for feature extraction combined with a kernel-based novelty detector. Results: The group results showed that the habituation correlates degrees, i.e., relative changes within the sweep sequences, were significantly different among 60dB SPL, 70dB SPL, 80dB SPL, and 90dB SPL stimulation level, independently from the intensity related amplitude information in the averaged LAEPs. At these particular intensities, 60% of the subjects show the correlation between the novelty measures and the stimulation levels resembles a loudness scaling function, in reverse. In this paper, we have found a correlation in between the novelty measures and loudness perception as well. We have found that high ranges of loudness levels such as loud, upper level and too loud show generally 4.88% of novelty measures and comfortable ranges of loudness levels, i.e., soft, comfortable but soft, comfortable loud and comfortable but loud are generally have 12.29% of novelty measures. Additionally, we demonstrated that our sweep-to-sweep basis of post processing scheme is reliable for habituation extraction and offers an advantage of reducing experimental time as the proposed scheme need less than 20% of single sweeps in comparison to the amount that are commonly used in arithmetical average for a meaningful result. Conclusions: We assessed the feasibility of habituation correlates for an objective loudness scaling. With respect to this first feasibility study, the presented results are promising when using the described signal processing and machine learning methodology. For the group results, the novelty measures approach is able to discriminate 60dB, 70dB, 80dB and 90dB stimulated sweeps. In addition, a correlation between the novelty measures and the subjective loudness scaling is observed. However, more loudness perception and frequency specific experiments need to be conducted to determine the UCL novelty measures threshold as well as clinically oriented studies are necessary to evaluate whether this approach might be used in the objective hearing instrument fitting procedures.