Get the slides: ABOUT THE TALK Over the past decade we have observed an increasing interest in developing technologies for automatic emotion recognition. The capacity of automatically recognizing emotions has many of applications in environments where machines need to interact and collaborate with humans. However, how can machines recognize emotions? In this talk I will give a brief introduction to Affective Computing (also known as Emotional Artificial Intelligence), the discipline that studies and develops systems and devices that can recognize, interpret, process or simulate emotions or feelings. After this I will talk about some research projects related with Emotion Recognition. In particular I will focus the attention on emotion and sentiment recognition systems based on Computer Vision and Natural Language Processing using Deep Learning. Finally, I will talk about possible applications of emotion recognition technologies. ABOUT THE SPEAKER Agata Lapedriza is a Professor at the Universitat Oberta de Catalunya. She received her MS degree in Mathematics at the Universitat de Barcelona and her Ph.D. degree in Computer Science at the Computer Vision Center, at the Universitat Autonoma Barcelona. She was working as a Visiting Researcher in the Computer Science and Artificial Intelligence Lab, at the Massachusetts Institute of Technology (MIT), from 2012 until 2015. Currently she is also a Visiting Researcher at the Affective Computing Group at MIT Medialab, where she leads the project of Emotion Recognition in Context. At MIT, she also colallaborates in different projects related to Human-Robot Interaction and Machine Perception. Her research interests are related [More]
Presentation on Facial Emotion Recognition System Using Machine Learning!! (Created By) Manisha Singh Himanshu Tuli Nidhi Singh
Here is the GitHub repository of the project:
iMotions and Stanford University team up to make use of eye tracking glasses and facial expression analysis inside one of the most advanced driving simulators. See how data is being combined across multiple sources to perform in-depth analysis and generate unique insights into a driver’s experience. This particular simulator is using data from devices & technology like eye-tracking glasses, facial coding software, as well as events generated from the user operating the vehicle. For instance, when the driver of the car presses on the gas to accelerate, the corresponding data is synchronized with sensors using the API. Want to find out more? Read more about the technology behind these studies at iMotions For more questions Contact us: Let’s get Social: Linkedin: Facebook: Twitter:
This is a presentation of the Facial Emotion Recognition CNN that I built. GitHub repository :
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Product test with three different testers. They have to rate two different portions of chocolate. We capture their emotions with a webcam and the offline software for desktop computer process the information instantly. We obtain data and metrics in real-time.
This is the project that takes our voice as input and gives the Emotion as output.
Real-time Facial Emotion Detection from Facial Expressions Asset is an open source software component that is developed at the Open University of the Netherlands. This work has been partially funded by the EC H2020 project RAGE (Realising an Applied Gaming Eco-System);; Grant agreement No 644187. This software component has the following advantages: 1. This real-time emotion detection asset is a client side software component that can detect emotions from players’ faces. 2. You can use it for instance in games for communication training or conflict management. Or for collecting emotion data during play-testing. 3. The software detects emotions in real-time and it returns a string representing six basic emotions: happiness, sadness, surprise, fear, disgust, and anger. It can also detect the neutral face. 4. The presence of multiple players would not be a problem as the software component can detect multiple faces and their emotions at the same time. 5. As inputs it may use the player’s webcam stream. But, it can also be used with a single image file, or with a recorded video file. 6. The emotion detection is highly accurate: the accuracy is over 83%, which is comparable with human judgment. 7. The software is written in C-Sharp. It runs in Microsoft Windows 7, 8, and 10, and it can be easily integrated in many game engines, including, for instance Unity3D. 8. This software uses the Apache-2 open source license, which means that you can use it for free, even in commercial applications. 9. The real-time [More]
⭐️ Content Description ⭐️ In this video, I have explained about speech emotion recognition analysis using python. This is a classification project in deep learning. I have build a LSTM neural network to build a classifier. GitHub Code Repo: Dataset link: 🔔 Subscribe: 🗓️ 1:1 Consultation with Me: 📷 Instagram: 🔣 Linkedin: 🎯 GitHub: 🎬 Share: ⚡️ Data Structures & Algorithms tutorial playlist: 😎 Hackerrank problem solving solutions playlist: 🤖 ML projects tutorial playlist: 🐍 Python tutorial playlist: 💻 Machine learning concepts playlist: ✍🏼 NLP concepts playlist: 🕸️ Web scraping tutorial playlist: Make a small donation to support the channel 🙏🙏🙏:- 🆙 UPI ID: hackersrealm@apl 💲 PayPal: 🕒 Timeline 00:00 Introduction to Speech Emotion Recognition 03:51 Import Modules 06:20 Load the Speech Emotion Dataset 12:34 Exploratory Data Analysis 25:20 Feature Extraction using MFCC 38:20 Creating LSTM Model 45:37 Plot the Model Results 49:15 End #speechemotionrecognition #machinelearning #hackersrealm #deeplearning #classification #lstm #datascience #model #project #artificialintelligence #beginner #analysis #python #tutorial #aswin #ai #dataanalytics #data #bigdata #programming #datascientist #technology #coding #datavisualization #computerscience #pythonprogramming #analytics #tech #dataanalysis #iot #programmer #statistics #developer #ml #business #innovation #coder #dataanalyst
Supervector Dimension Reduction for Efficient Speaker Age Estimation Based on the ASS presents a novel dimension reduction method which aims to improve the accuracy and the efficiency of speaker’s age estimation systems based on speech signal. Two different age estimation approaches were studied and implemented; the first, age-group classification, and the second, precise age estimation using regression. These two approaches use the Gaussian mixture model (GMM) supervectors as features for a support vector machine (SVM) model. When a radial basis function (RBF) kernel is used, the accuracy is improved compared to using a linear kernel; however, the computation complexity is more sensitive to the feature dimension. Classic dimension reduction methods like principal component analysis (PCA) and linear discriminant analysis (LDA) tend to eliminate the relevant feature information and cannot always be applied without damaging the model’s accuracy. #AI #DeepLearning #Tensorflow #Matlab #AI #Deep Learning # Tensorflow # Python # Matlab “Emotion detector in MATLAB ” in this video it is shown that how one can detect emotions of human in MATLAB with respect to results saved in the database. Visit our website to know more about our services at: Direct at +91- 9872993883 WhatsApp at +91- 9872993883 E-mail me at – How we at RIS help you with Emotion Detection Implementation | AI related thesis? RIS AI is the best option to drive away all the confusion and thesis troubles. Usually, we provide online research paper writing services to make thesis work easier for you. No doubt, we are one stop solution for all your PhD and M. Tech thesis writing needs. In addition, RIS AI provides you the best Online Research Paper Writing services that you can ever imagine. It is because, we have a team of experts who can deliver quality work and that too, in a limited time. Not just that, we consider your budget and guide you with the right services that you really need. We offer customized thesis solutions to perfectly match all your thesis requirements. Quality comes with a price. And a good quality thesis is the result of attention to details, perfection and complete dedication. We work on the thesis projects in the best possible way. Why do you need Thesis Assistance Online? Online world has expanded itself and has made it quite easier for everyone [More]
Live knowledge sharing sessions by industry experts on latest and trending skills and technologies. This One Hour session will provide the participants with an insight into the latest industrial standards and applications on the desired domain. Reg link : For E_Certificate Feedback form entry is mandatory For Previous Videos:- #NoviTech #SpeechEmotionDetection #MachineLearning
Hello Friends, In this episode we are going to do Emotion Detection using Convolutional Neural Network(CNN). I will do the step by step implementation starting for the dataset download, accessing data set, preprocessing images, designing CNN, training CNN , saving trained model and using that saved model to do the emotion detection on video or live stream. Code link : Emotion detection in 5 Lines using pre-trained model -: =========== Time Code =========== 00:01 Introduction to Emotion Detection using CNN 01:21 FER 2013 Facial Expression Dataset 04:12 files in emotion detection project 05:52 Image preprocessing using Image Data Generator 08:09 Design/Create Convolution Neural Network for Emotion Detection 10:33 Train out CNN with FER 2013 Dataset / Train CNN for Emotion Detection 11:59 Save the trained model weights and structure 13:08 Test Trained Emotion Detection model 14:15 Load saved model 15:05 Access Video or Camera Feed for testing Emotion Detection model 16:20 Face detection with Haarcascade classifier 18:16 Detect and Highlight each face on video 20:06 Predict Emotion using model 20:21 Display Emotion on video 21:53 Emotion Detection Demo 24:58 emotion detection improvisations Stay tuned and enjoy Machine Learning !!! Cheers !!! #emotiondetection #CNN #DeepLearning Connect with me, ☑️ YouTube : ☑️ Facebook : ☑️ Instagram : ☑️ Twitter : ☑️ Telegram: For Business Inquiries : Best book for Machine Learning : 🎥 Playlists : ☑️Machine Learning Basics ☑️Feature Engineering/ Data Preprocessing ☑️OpenCV Tutorial [Computer Vision] ☑️Machine Learning Algorithms [More]
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Welcome to this new video series in which we will be using Natural Language Processing or it’s called NLP in short. to analyse emotions and sentiments of given text. After completing this videos series – 1) You will be able to analyse different emotions present in an essay like sadness, happiness, jealousy etc 2) You will be able to find out the dominant emotion in the text 3) You will be able to plot those emotions on a graph 4) And you will also be able to tell whether the whole text is a positive or negative emotion 5) And finally you will also be able scrap tweets with a hashtag and find out the public opinion on that hashtag. For example you can search for #donaldtrump and find out whether that emotion is associated with a positive or a negative sentiment. First we will be doing all the natural language processing and sentiment analysis on our own without the use of a library or a package. So that you guys properly understand the concepts of NLP and then we can go on to use NLTK library to shorten our work. Source Code – Next video – Installing Python and Pycharm Full playlist – Subscribe – Website – Instagram – #python #nltk #nlp
🔥Edureka PG Diploma in Artificial Intelligence & ML from E & ICT Academy NIT Warangal(Use Code: YOUTUBE20): This Edureka video on ‘Emotion Detection using OpenCV & Python’ will give you an overview of Emotion Detection using OpenCV & Python and will help you understand various important concepts that concern Emotion Detection using OpenCV & Python Following pointers are covered in this Emotion Detection using OpenCV & Python: 00:00:00 Agenda 00:01:54 Introduction to Deep Learning 00:04:14 What is Image Processing? 00:04:58 Libraries used in Project 00:07:30 Steps to execute the Project 00:08:47 Implementation ———————————— Github link for codes: dataset link: ———————————— 🔹Check Edureka’s Deep Learning & TensorFlow Tutorial playlist here: 🔹Check Edureka’s Deep Learning & TensorFlow Tutorial Blog Series: 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: Twitter: LinkedIn: Instagram: Facebook: SlideShare: Castbox: Meetup: ———𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧——— 🔵 Data Science Online Training: 🟣 Python Online Training: 🔵 AWS Online Training: 🟣 RPA Online Training: 🔵 DevOps Online Training: 🟣 Big Data Online Training: 🔵 Java Online Training: ———𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬——— 🟣Machine Learning Engineer Masters Program: 🔵DevOps Engineer Masters Program: 🟣Cloud Architect Masters Program: 🔵Data Scientist Masters Program: 🟣Big Data Architect Masters Program: 🔵Business Intelligence Masters Program: —————–𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏GD 𝐂𝐨𝐮𝐫𝐬𝐞𝐬————— 🔵Artificial and Machine Learning PGD: #edureka #edurekadeeplearning #deeplearning #EmotionDetectionusingOpenCV&Python #RealTimeEmotionDetection #machinelearningpretrainedmodels #deeplearningtutorial #edurekatraining ——————————————————————– Why Machine Learning & [More]
Sam and Emma host Kate Crawford, Research Professor at the University of Southern California Annenberg, to discuss her recent book Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, on our relationship with big tech, and the concept of the AI industry as a continuation of the extractive practices and power dynamics in the workplace that we have been building for centuries. We stream our live show every day at 12 PM ET. We need your help to keep providing free videos! Support the Majority Report’s video content by going to Watch the Majority Report live M–F at 12 p.m. EST at or listen via daily podcast at http://Majority.FM Download our FREE app: SUPPORT the show by becoming a member: We Have Merch!!! LIKE us on Facebook: FOLLOW us on Twitter: SUBSCRIBE to us on YouTube:
The Pentagon’s research arm has pumped $1 million into a contract to build an AI tool meant to decode and predict the emotions of allies and enemies. It even wants the AI app to advise generals on major military decisions. DARPA’s backing is the starting pistol for a race with the government and startups to use AI to predict emotions but the science behind it is deeply controversial. Some say it’s entirely unproven, making military applications that much riskier. The previously-unreported work is being carried out under a DARPA project dubbed PRIDE, short for the Prediction and Recognition of Intent, Decision and Emotion. The aim is to create an AI that can understand and predict reactions of a group, rather than an individual, and then offer guidance on what to do next. Think of a military leader who wants to know how a political faction or a whole country would react should he or she take an aggressive action against their leader. In PRIDE, the emotion detection is not for an individual. It’s more as a collective group and even at a national level,” says Dr. Kalyan Gupta, president and founder of Knexus. “To think about, you know, whether a nation state is either angry or agitated.” And it’s no small fry initiative; the plan is for PRIDE to provide recommendations for “international courses of action,” according to a contract description. Whilst DARPA’s project is largely looking at sentiment elicited from text and information posted online, a handful of startups, [More]
Despite the great progress made in artificial intelligence, we are still far from having a natural interaction between man and machine, because the machine does not understand the emotional state of the speaker. Speech emotion detection has been drawing increasing attention, which aims to recognize emotion states from speech signal. The task of speech emotion recognition is very challenging, because it is not clear which speech features are most powerful in distinguishing between emotions. We utilize deep neural networks to detect emotion status from each speech segment in an utterance and then combine the segment-level results to form the final emotion recognition results. The system produces promising results on both clean speech and speech in gaming scenario.
Sentiment analysis is an active research field where researchers aim to automatically determine the polarity of text [1], either as a binary problem or as a multi-class problem where multiple levels of positiveness and negativeness are reported. Recently, there is an increasing interest in going beyond sentiment, and analyzing emotions such as happiness, fear, anger, surprise, sadness and others. Emotion detection has many use cases for both enterprises and consumers. The best-known examples are customer service performance monitoring [2], and social media analysis [3]. In this talk, we present a new algorithm based on deep learning, which not only outperforms state-of-the-art method [4] in emotion detection from text, but also automatically decides on length of emotionally-intensive text blocks in a document. Our talk presents the problem by examples, with business motivations related to the Microsoft Cognitive Services suite. We present a technique to capture both semantic and syntactic relationships in sentences using word embeddings and Long Short-Term Memory (LSTM) based modeling. Our algorithm exploits lexical information of emotions to enrich the data representation. We present empirical results based on ISAER and SemEval-2007 datasets [5,6]. We then motivate the problem of detecting emotionally-intensive text blocks of various sizes, along with an entropy-based technique to solve it by determining the granularity on which the emotions model is applied. We conclude with a live demonstration of the algorithm on diverse types of data: interviews, customer service, and social media.
Using only speech samples, machine learning can detect emotions in a speaker’s voice. This session will outline modeling challenges including label uncertainty and robustness to non-emotional latent factors, and present an adversarial auto-encoder learning approach that can be applied to a wide range of models. Session Speakers: Viktor Rozgic, Chao Wang (Session A07)