scholarly journals Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence

2016 ◽  
Vol 10 ◽  
Author(s):  
Chun Liang ◽  
Brian Earl ◽  
Ivy Thompson ◽  
Kayla Whitaker ◽  
Steven Cahn ◽  
...  
Keyword(s):  
Change Detection ◽  
Frequency Change ◽  
Electrophysiological Evidence ◽  
Better Than

Sound frequency change detection in fetuses and newborns, a magnetoencephalographic study

NeuroImage ◽  
2005 ◽  
Vol 28 (2) ◽  
pp. 354-361 ◽  
Author(s):  
Rossitza Draganova ◽  
Hari Eswaran ◽  
Pamela Murphy ◽  
Minna Huotilainen ◽  
Curtis Lowery ◽  
...  
Keyword(s):  
Change Detection ◽  
Sound Frequency ◽  
Frequency Change

scholarly journals A KERNEL-BASED SIMILARITY MEASURING FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 999-1006
Author(s):  
Xiaodan Shi ◽  
Guorui Ma ◽  
Fenge Chen ◽  
Yanli Ma
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Comparison Method ◽  
Kernel Functions ◽  
Multidimensional Data ◽  
Remote Sensing Images ◽  
Model Free ◽  
Change Vector ◽  
Similarity Measuring ◽  
Better Than

This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the feature vector extracted from bi- temporal images. PD is compared by defined kernel functions without immediate PD estimation. This algorithm is model-free and it can process multidimensional data, and is fit for the images with rich texture in particular. Experimental results show that overall accuracy of the algorithm is 98.9 %, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7 % and 95.9 % respectively.

A Approach to Change Detection for HR Image

2014 ◽  
Vol 971-973 ◽  
pp. 1449-1453
Author(s):  
Zuo Wei Huang ◽  
Shu Guang Wu ◽  
Tao Xin Zhang
Keyword(s):  
Change Detection ◽  
Detection Rate ◽  
False Detection Rate ◽  
False Detection ◽  
High Detection Rate ◽  
High Detection ◽  
Self Adaptation ◽  
Ica Algorithm ◽  
Different Characteristics ◽  
Better Than

Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together, In order to accord with the condition of hyperspectral imagery，the paper developed an optimized ICA algorithm for change detection to describe the statistical distribution of the data. By processing these abundance maps, change of different classes of objects can be obtained..A approach is capable of self-adaptation, and can be applied to hyperspectral images with different characteristics. Experiment results demonstrate that the ICA-based hyperspectral change detection performs better than other traditional methods with a high detection rate and a low false detection rate.

scholarly journals Cortical Processing of Frequency Changes Reflected by the Acoustic Change Complex in Adult Cochlear Implant Users

2018 ◽  
Vol 23 (3) ◽  
pp. 152-164 ◽  
Author(s):  
Chun Liang ◽  
Lisa M. Houston ◽  
Ravi N. Samy ◽  
Lamiaa Mohamed Ibrahim Abedelrehim ◽  
Fawen Zhang
Keyword(s):  
Speech Perception ◽  
Cochlear Implant ◽  
Change Detection ◽  
Detection Threshold ◽  
Auditory Evoked Potential ◽  
Frequency Change ◽  
Large Variability ◽  
Frequency Changes ◽  
Acoustic Change Complex ◽  
Electroencephalographic Recording

The purpose of this study was to examine neural substrates of frequency change detection in cochlear implant (CI) recipients using the acoustic change complex (ACC), a type of cortical auditory evoked potential elicited by acoustic changes in an ongoing stimulus. A psychoacoustic test and electroencephalographic recording were administered in 12 postlingually deafened adult CI users. The stimuli were pure tones containing different magnitudes of upward frequency changes. Results showed that the frequency change detection threshold (FCDT) was 3.79% in the CI users, with a large variability. The ACC N1’ latency was significantly correlated with the FCDT and the clinically collected speech perception score. The results suggested that the ACC evoked by frequency changes can serve as a useful objective tool in assessing frequency change detection capability and predicting speech perception performance in CI users.

scholarly journals Frequency change detection and speech perception in cochlear implant users

2019 ◽  
Vol 379 ◽  
pp. 12-20 ◽  
Author(s):  
Fawen Zhang ◽  
Gabrielle Underwood ◽  
Kelli McGuire ◽  
Chun Liang ◽  
David R. Moore ◽  
...  
Keyword(s):  
Speech Perception ◽  
Cochlear Implant ◽  
Change Detection ◽  
Frequency Change

Auditory Change Detection: Simple Sounds Are Not Memorized Better Than Complex Sounds

2008 ◽  
Vol 19 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Laurent Demany ◽  
Wiebke Trost ◽  
Maja Serman ◽  
Catherine Semal
Keyword(s):  
Change Detection ◽  
Complex Sounds ◽  
Auditory Change Detection ◽  
Better Than

scholarly journals Electrophysiological evidence for different qualities of change detection and change blindness

2010 ◽  
Vol 7 (9) ◽  
pp. 648-648
Author(s):  
N. A. Busch ◽  
I. Fruend ◽  
C. S. Herrmann
Keyword(s):  
Change Detection ◽  
Change Blindness ◽  
Electrophysiological Evidence

scholarly journals Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

2021 ◽  
Author(s):  
RG Negri ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Pattern Analysis ◽  
Detection Method ◽  
Simulated Data ◽  
Detection Methods ◽  
Practical Significance ◽  
Significance Level ◽  
Natural Factors ◽  
Better Than

© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

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