#Emotion-recognition
Showing 33 of 33 repositories tagged #emotion-recognition, ranked by stars
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
Industrial-grade speech recognition toolkit: 170x realtime, 50+ languages, speaker diarization, emotion detection, streaming, and OpenAI-compatible API.
DELTA is a deep learning based natural language and speech processing platform. LF AI & DATA Projects: https://lfaidata.foundation/projects/delta/
The neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)
Real-time Facial Emotion Detection using deep learning
Real time emotion recognition
A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
Winner of Mozilla's $50,000 prize for AI
Official repository accompanying a CVPR 2022 paper EMOCA: Emotion Driven Monocular Face Capture And Animation. EMOCA takes a single image of a face as input and produces a 3D reconstruction. EMOCA sets the new standard on reconstructing highly emotional images in-the-wild
Emotion recognition using DNN with tensorflow
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
Turn your facial expression into an emoji
Example projects built with the Hume AI APIs
A curated list of awesome affective computing 🤖❤️ papers, software, open-source projects, and resources
Identify the emotion of multiple speakers in an Audio Segment
This is my reading list for my PhD in AI, NLP, Deep Learning and more.
Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
Flask web app for recommending music based on your facial expressions using FER 2013 dataset and Spotify api
PyTorch implementation of TSception
Deep-learning models of NTUA-SLP team submitted in SemEval 2018 tasks 1, 2 and 3.
Computer Vision project that detects emotion, age and gender after detecting faces.
Emotions recognition from audio and text files (only russian language)
Using Deep Learning for Emotion Classification on EEG signals (SEED Dataset). CNN, RNN, Hybrid model, and Ensemble
An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP
EEG-Based Emotion Recognition
This is a Python 3 based project to display facial expressions by performing fast & accurate face detection with OpenCV using a pre-trained deep learning face detector model shipped with the library.
Face detection from webcam in browser using javascript library face-api.js
Group Emotion Recognition using deep neural networks and Bayesian classifiers.
Predicting various emotion in human speech signal by detecting different speech components affected by human emotion.
Facial Expression Recognition
"Emotion-based-Music-Player" is a unique project designed to enhance music listening experiences. By analyzing user emotions, this player dynamically selects music tracks that match the user's mood. Through innovative algorithms, it tailors playlists to evoke desired emotional responses, offering a personalized journey through music.
Real-time behavioral intelligence for call centers. Transcribes support calls, redacts PII, extracts emotional tone, classifies issues, and delivers insight-rich dashboards — powered by GPT-3.5 (cheap tokens), Whisper, DuckDB, and a polished React+TypeScript frontend. No Azure. No Power BI. No vendor lock-in. Just full-stack AI that runs local.
Emotional recognition using electroencephelography (EEG)