G.722: a new CCITT coding standard for digital transmission of wideband audio signals

1988 ◽  
Vol 26 (1) ◽  
pp. 8-15 ◽  
Author(s):  
P. Mermelstein
Keyword(s):  
Digital Transmission ◽  
Audio Signals

Outreach Opportunities Using the Instructional SEM at Iowa State University

1996 ◽  
Vol 54 ◽  
pp. 412-413
Author(s):  
L. S. Chumbley ◽  
M. Meyer ◽  
K. Fredrickson ◽  
F.C. Laabs
Keyword(s):  
Materials Science ◽  
State University ◽  
Digital Cameras ◽  
Audio Signals ◽  
Iowa State University ◽  
Cornell University ◽  
Sem Image ◽  
Local Schools ◽  
Materials Science And Engineering ◽  
Video And Audio

The development of a scanning electron microscope (SEM) suitable for instructional purposes has created a large number of outreach opportunities for the Materials Science and Engineering (MSE) Department at Iowa State University. Several collaborative efforts are presently underway with local schools and the Department of Curriculum and Instruction (C&I) at ISU to bring SEM technology into the classroom in a near live-time, interactive manner. The SEM laboratory is shown in Figure 1.Interactions between the laboratory and the classroom use inexpensive digital cameras and shareware called CU-SeeMe, Figure 2. Developed by Cornell University and available over the internet, CUSeeMe provides inexpensive video conferencing capabilities. The software allows video and audio signals from Quikcam™ cameras to be sent and received between computers. A reflector site has been established in the MSE department that allows eight different computers to be interconnected simultaneously. This arrangement allows us to demonstrate SEM principles in the classroom. An Apple Macintosh has been configured to allow the SEM image to be seen using CU-SeeMe.

Analysis of timing jitter in digital transmission systems

2001 ◽  
Vol 84 (8) ◽  
pp. 1-13
Author(s):  
Kazuhiro Miyauchi ◽  
Isamu Wakabayashi ◽  
Hiroki Shibayama
Keyword(s):  
Digital Transmission ◽  
Timing Jitter ◽  
Transmission Systems

scholarly journals Accurate Tongue–Palate Pressure Sensing Device to Study Speech Production and Swallowing in Patients with Complete Denture

2021 ◽  
Author(s):  
Bharat Mirchandani ◽  
Pascal Perrier ◽  
Brigitte Grosgogeat ◽  
Christophe Jeannin
Keyword(s):  
Speech Production ◽  
Temporal Variations ◽  
Complete Denture ◽  
Positive Pressure ◽  
Single Subject ◽  
Audio Signals ◽  
Motor Tasks ◽  
Pressure Sensing ◽  
French Speaking ◽  
Swallowing Pressure

Abstract Objectives The mechanical interactions between tongue and palate are crucial for speech production and swallowing. In this study, we present examples of pressure signals that can be recorded with our PRESLA system (PRESLA holds for the French expression “PRESsion de la LAngue” [Pressure from the tongue]) to assess these motor functions, and we illustrate which issues can be tackled with such a system. Materials and Methods A single French-speaking edentulous subject, old wearer of a complete denture, with no speech production and swallowing disorders, was recorded during the production of nonsense words including French alveolar fricatives, and during dry and water swallowing. The PRESLA system used strain-gauge transducers that were inserted into holes drilled in the palatal surface of a duplicate of the prosthesis at six locations that were relevant for speech production and swallowing. Pressure signals were postsynchronized with the motor tasks based on audio signals. Results Patterns of temporal variations of the pressure exerted by the tongue on the palate are shown for the two studied motor tasks. It is shown for our single subject that patterns for fricative /s/ are essentially bell shaped, whereas pressure signals observed for water swallow begin with a maximum followed by a slow decrease during the rest of the positive pressure phase. Pressure magnitude is almost 20 times larger for water swallow than for /s/ production. Conclusions This study illustrates the usefulness of our PRESLA system for studying speech production and swallowing motor control under normal and pathological conditions.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 676
Author(s):  
Andrej Zgank
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Markov Models ◽  
Audio Signal ◽  
Audio Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Animal Activity ◽  
The Impact ◽  
Acoustic Classification ◽  
Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

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