SIGNAL-to-noise ratioThis study aimed to compare the Hebrew version of the digits-in-noise (DIN) thresholds among cochlear implant (CI) users and their normal-hearing (NH) counterparts, explore the influence of age on these thresholds, examine the effects of early aud...
The digits-in-noise (DIN) test also referred to as the digit triplet test (DTT) is a speech audiometry test that involves recognising combinations of digits (usually three, called a triplet) presen...
Research Design: Participants completed the Hearing Handicap Inventory for Adults (HHIA) questionnaire, audiometric testing in a sound booth, and computerized DIN testing. During the DIN test, sequences of three spoken digits were presented in noise via headphones at varying signal-to-noise ratios ...
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This is especially true when the initial choice of hyper-parameters produces results no better than random noise. Suppose we try the successful 30 hidden neuron network architecture from earlier, but with the learning rate changed to : >>> net = network.Network([784, 30, 10]) >>> net....
The ALOPEX process is a broadly defined optimization procedure which simultaneously varies several parameters based on single value feedback and noise appl... TJ Dasey,E Micheli-Tzanakou - IEEE 被引量: 41发表: 1989年 Geometric isomers of substituted triphenylethylenes and antiestrogen action. The...
This is especially true when the initial choice of hyper-parameters produces results no better than random noise. Suppose we try the successful 30 hidden neuron network architecture from earlier, but with the learning rate changed to η=100.0η=100.0:>>> net = network.Network([784, 30, 10]...
For further analysis, we passed the test dataset through the “classify many” option provided by DIGITS to obtain a class score per sample. Figure 2: End-to-End deep learning pipeline to classify Alzheimer’s Disease fMRI samples. Data Acquisition and Preprocessing In this work, we selected ...
Finally the conclusions in section 5. 2 Multiple Specialised Classifiers The basic aim of the proposed Multiple Specialist Classifier (MSC), is to obtain a multi-classifier formed by specialised simple classifiers for each type of noise pro- duced by an image. Each specialist obtains the features...