Automatic recognition of depression based on audio and video:A review

Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

VB环境下Audio/Video压缩数据流播放技术的应用

介绍了在 Windwos操作系统中 ,利用 VB自身条件和原有控件 ,灵活调用 Windows下的动态链接库开发

Real-time Audio &Video Transmission System Based on Visible Light Communication

With the increasing popularity of solid sate lighting devices, Visible Light Communication (VLC) is globally recognized as an advanced and promising technology to realize short-range, high speed as well as large capacity wireless data transmission. In this paper, we propose a prototype of real-time audio and video broadcast system using inexpensive commercially available light emitting diode (LED) lamps. Experimental results show that real-time high quality audio and video with the maximum distance of 3 m can be achieved through proper layout of LED sources and improvement of concentration effects. Lighting model within room environment is designed and simulated which indicates close relationship between layout of light sources and distribution of illuminance.

Integrating Audio-Visual Features and Text Information for Story Segmentation of News Video

Video data are composed of multimodal information streams including visual, auditory and textual streams, so an approach of story segmentation for news video using multimodal analysis is described in this paper. The proposed approach detects the topic-caption frames, and integrates them with silence clips detection results, as well as shot segmentation results to locate the news story boundaries. The integration of audio-visual features and text information overcomes the weakness of the approach using only image analysis techniques. On test data with 135 400 frames, when the boundaries between news stories are detected, the accuracy rate 85.8% and the recall rate 97.5% are obtained. The experimental results show the approach is valid and robust.

Content-Based Hierarchical Analysis of News Video Using Audio and Visual Information

A schema for content-based analysis of broadcast news video is presented. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.

Study on an Audio and Video Network Monitoring System for Weather Modification Operation

An audio and video network monitoring system for weather modification operation transmitting information by 3G, ADSL and Internet has been developed and applied in weather modification operation of Tai＇an City. The all-in-one machine of 3G audio and video network highly integrates all front-end devices used for audio and video collection, communication, power supply and information storage, and has advantages of wireless video transmission, clear two-way voice intercom with the command center, waterproof and dustproof function, simple operation, good portability, and long working hours. Compression code of the system is transmitted by dynamic bandwidth, and compression rate varies from 32 kbps to 4 Mbps under different network conditions. This system has forwarding mode, that is, monitoring information from each front-end monitoring point is trans- mitted to the server of the command center by 3G/ADSL, and the server codes＇and decodes again, then beck-end users call images from the serv- er, which can address 3G network stoppage caused by many users calling front-end video at the same time. In addition, the system has been ap- plied in surface weather modification operation of Tai＇an City, and has made a great contribution to transmitting operation orders in real time, monitoring, standardizing and recording operating process, and improving operating safety.

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

audio和video

电视上看到一则某种牌子的电器之广告,内中有audio和video两个字,借贵刊一角谈一谈。不用说,这两个字都是和电器有关的。audio与“音”有关系,video则和“影”有关。 audio是指由声音、机械、或电力所造成的频率(audio frs-quency),具有这种频率的声波每秒钟振动十五至二万次,也就是所谓低周波,是人类所能听得见的。在电器制品中,andio 特指电唱机、收音机或电视机的发音部分,平常人们所说的音响设备就叫audio equipment。因此,audio现用来泛指一般与音响有关的东西。

Audio Description for Educational Videos on COVID-19 Response:A Corpus-Based Study on Linguistic and Textual Idiosyncrasies

Audio description(AD),unlike interlingual translation and interpretation,is subject to unique constraints as a spoken text.Facilitated by AD,educational videos on COVID-19 anti-virus measures are made accessible to the visually disadvantaged.In this study,a corpus of AD of COVID-19 educational videos is developed,named“Audio Description Corpus of COVID-19 Educational Videos”(ADCCEV).Drawing on the model of Textual and Linguistic Audio Description Matrix(TLADM),this paper aims to identify the linguistic and textual idiosyncrasies of AD themed on COVID-19 response released by the New Zealand Government.This study finds that linguistically,the AD script uses a mix of complete sentences and phrases,the majority being in Present Simple tense.Present participles and the“with”structure are used for brevity.Vocabulary is diverse,with simpler words for animated explainers.Third-person pronouns are common in educational videos.Color words are a salient feature of AD,where“yellow”denotes urgency,and“red”indicates importance,negativity,and hostility.On textual idiosyncrasies,coherence is achieved through intermodal components that align with the video’s mood and style.AD style varies depending on the video’s purpose,from informative to narrative or expressive.

Stylistic Analysis of Internet News——Taking Internet Video Newsand Internet Audio News as Examples

With the rapid development of Internet around the world, network is transmitting all kinds of information to human beings nowadays. Net news, also called cyber news is affecting people’s expression of daily English. A large number of cyber words, phrases even sentences, which are different from conventional English, are formed and become popular in the cyber world. This paper discusses different markers of net news by taking Internet video news and Internet audio news as examples so that the readers can fully understand the properties of net news.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

跃威USB VIDEO AUDIO延长器

亲爱的,俺把电脑延长了科技的发展有时候总会让人措手不及。当我还在犹豫到底是否需要斥“巨资”购买一台HDTV的时候,发现只需要用USB VIDEO AUDIO延长器就可以把书房的电脑延伸到客厅中,—切问题都迎刃而解了。

访森海塞尔中国内地地区专业音频Audio for Video销售负责人贾毅阳及诺音曼中国内地地区销售负责人储海涛

BIRTV2023期间,在中央广播电视总台展台《现代电视技术》现场访谈间,本刊对森海塞尔中国内地地区专业音频Audio for Video销售负责人贾毅阳以及诺音曼中国内地地区销售负责人储海涛进行了采访,采访围绕两个品牌的产品亮点、优势及市场定位等话题展开。曹徐洋:在今年的BIRTV展会上,森海塞尔和诺音曼的展台都展出了大量优秀的产品,这些产品里有哪些是重点推出的?请介绍一下它们的主要亮点。

The College Video English Visual-audio-oral Learning System

In order to respond to the need of social development,cultivate international talents,and improve the current English teaching mode,this paper studies video English visual-audio-oral learning system based on machine learning from the perspective of teaching and learning video English.It mainly analyzes the knowledge discovery process of machine learning,the design and application of video English visual-audio-oral learning system.It is found that the video English visual-audio-oral learning system based on machine learning has much higher level of practicality and efficiency compared with the traditional English language teaching in real life.The application of this system can also be of great significance in changes on language learning modes and methods in the future.