scholarly journals Why Early Tactile Speech Aids May Have Failed: No Perceptual Integration of Tactile and Auditory Signals

2018 ◽  
Vol 9 ◽  
Author(s):  
Aurora Rizza ◽  
Alexander V. Terekhov ◽  
Guglielmo Montone ◽  
Marta Olivetti-Belardinelli ◽  
J. Kevin O’Regan
Keyword(s):  
Perceptual Integration ◽  
Auditory Signals

Influence of imaged pictures and sounds on detection of visual and auditory signals.

1970 ◽  
Vol 83 (3, Pt.1) ◽  
pp. 458-464 ◽  
Author(s):  
Sydney J. Segal ◽  
Vincent Fusella
Keyword(s):  
Auditory Signals

The Role of Learning in the Production and Comprehension of Auditory Signals by Pinnipeds

2006 ◽  
Vol 32 (4) ◽  
pp. 483-490 ◽  
Author(s):  
Kristy L. Lindemann ◽  
Colleen Reichmuth Kastak ◽  
Ronald J. Schusterman
Keyword(s):  
Auditory Signals

Complex Auditory Signals

1994 ◽  
Author(s):  
David M. Green
Keyword(s):  
Auditory Signals

scholarly journals Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1718
Author(s):  
Chien-Hsing Chou ◽  
Yu-Sheng Su ◽  
Che-Ju Hsu ◽  
Kong-Chang Lee ◽  
Ping-Hsuan Han
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
User Experience ◽  
Recognition System ◽  
Scene Recognition ◽  
Single Shot ◽  
Auditory Signals ◽  
Hot Weather ◽  
Viewing Experience ◽  
At Home

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

scholarly journals Erratum to: Evaluating perceptual integration: uniting response-time and accuracy-based methodologies

2014 ◽  
Vol 77 (1) ◽  
pp. 369-369 ◽  
Author(s):  
Ami Eidels ◽  
James T. Townsend ◽  
Howard C. Hughes ◽  
Lacey A. Perry
Keyword(s):  
Response Time ◽  
Perceptual Integration

Coal gangue recognition using multichannel auditory spectrogram of hydraulic support sound in convolutional neural network

2021 ◽  
Author(s):  
xu chen ◽  
Shibo Wang ◽  
Houguang Liu ◽  
Jianhua Yang ◽  
Songyong Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Characteristics ◽  
Coal Gangue ◽  
Hydraulic Support ◽  
Practical Applications ◽  
Network Training ◽  
Auditory Recognition ◽  
Auditory Signals ◽  
Fully Connected

Abstract Many data-driven coal gangue recognition (CGR) methods based on the vibration or sound of collapsed coal and gangue have been proposed to achieve automatic CGR, which is important for realizing intelligent top-coal caving. However, the strong background noise and complex environment in underground coal mines render this task challenging in practical applications. Inspired by the fact that workers distinguish coal and gangue from underground noise by listening to the hydraulic support sound, we propose an auditory model based CGR method that simulates human auditory recognition by combining an auditory spectrogram with a convolutional neural network (CNN). First, we adjust the characteristic frequency (CF) distribution of the auditory peripheral model (APM) based on the spectral characteristics of collapsed sound signals from coal and gangue and then process the sound signals using the adjusted APM to obtain inferior colliculus auditory signals with multiple CFs. Subsequently, the auditory signals of all CFs are converted into gray images separately and then concatenated into a multichannel auditory spectrum along the channel dimension. Finally, we input the multichannel auditory spectrum as a feature map to the two-dimensional CNN, whose convolutional layers are used to automatically extract features, and the fully connected layer and softmax layer are used to flatten features and predict the recognition result, respectively. The CNN is optimized for the CGR based on a comparison study of four typical types of CNN structures with different network training hyperparameters. The experimental results show that this method affords an accurate CGR with a recognition accuracy of 99.5%. Moreover, this method offers excellent noise immunity compared with typically used CGR methods under various noisy conditions.

Detection of Auditory Signals in Reproducible Noise

1966 ◽  
Vol 39 (2) ◽  
pp. 340-345 ◽  
Author(s):  
Sheila M. Pfafflin ◽  
M. V. Mathews
Keyword(s):  
Auditory Signals

Perceptual integration of identity, location and colour

1986 ◽  
Vol 48 (4) ◽  
pp. 189-200 ◽  
Author(s):  
E. A. Styles ◽  
D. A. Allport
Keyword(s):  
Perceptual Integration
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