DANN: Digital Audio Neural Network

2019 ◽  
Author(s):  
Zlatko Baracskai
Keyword(s):  
Neural Network ◽  
Digital Audio

scholarly journals Deep Convolutional Neural Network based Image Steganogrpahy Technique for Audio-Image Hiding Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 2187-2189
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Digital Image ◽  
Digital Audio ◽  
Least Significant Bit ◽  
Stego Image ◽  
Vast Number ◽  
Learning Techniques ◽  
Network Concept ◽  
Audio Files

Steganography is one expanding filed in the area of Data Security. Steganography has attractive number of application from a vast number of researchers. The most existing technique in steganogarphy is Least Significant Bit (LSB) encoding. Now a day there has been so many new approaches employing with different techniques like deep learning. Those techniques are used to address the problems of steganography. Now a day’s many of the exisiting algorithms are based on the image to data, image to image steganography. In this paper we hide secret audio into the digital image with the help of deep learning techniques. We use a joint deep neural network concept it consist of two sub models. The first model is responsible for hiding digital audio into a digital image. The second model is responsible for returning a digital audio from the stego image. Various vast experiments are conducted with a set of 24K images and also for various sizes of images. From the experiments it can be seen proposed method is performing more effective than the existing methods. The proposed method also concentrates the integrity of the digital image and audio files.

Clinical Success Using the Audio-Visual Feedback Training for Cluttering

2019 ◽  
Vol 4 (6) ◽  
pp. 1589-1594
Author(s):  
Yvonne van Zaalen ◽  
Isabella Reichel
Keyword(s):  
Visual Feedback ◽  
Clinical Success ◽  
Pause Duration ◽  
Digital Audio ◽  
Male Adolescents ◽  
Feedback Training ◽  
Positive Effects ◽  
Speech Monitoring ◽  
Adolescents And Adults

Purpose Among the best strategies to address inadequate speech monitoring skills and other parameters of communication in people with cluttering (PWC) is the relatively new but very promising auditory–visual feedback (AVF) training ( van Zaalen & Reichel, 2015 ). This study examines the effects of AVF training on articulatory accuracy, pause duration, frequency, and type of disfluencies of PWC, as well as on the emotional and cognitive aspects that may be present in clients with this communication disorder ( Reichel, 2010 ; van Zaalen & Reichel, 2015 ). Methods In this study, 12 male adolescents and adults—6 with phonological and 6 with syntactic cluttering—were provided with weekly AVF training for 12 weeks, with a 3-month follow-up. Data was gathered on baseline (T0), Week 6 (T1), Week 12 (T2), and after follow-up (T3). Spontaneous speech was recorded and analyzed by using digital audio-recording and speech analysis software known as Praat ( Boersma & Weenink, 2017 ). Results The results of this study indicated that PWC demonstrated significant improvements in articulatory rate measurements and in pause duration following the AVF training. In addition, the PWC in the study reported positive effects on their ability to retell a story and to speak in more complete sentences. PWC felt better about formulating their ideas and were more satisfied with their interactions with people around them. Conclusions The AVF training was found to be an effective approach for improving monitoring skills of PWC with both quantitative and qualitative benefits in the behavioral, cognitive, emotional, and social domains of communication.

Digital Audio and Self-Help for People Who Stutter

ASHA Leader ◽  
2008 ◽  
Vol 13 (14) ◽  
pp. 19-19 ◽  
Author(s):  
Greg Snyder ◽  
Peter Reitzes ◽  
Eric Jackson
Keyword(s):  
Digital Audio ◽  
Self Help

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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