There are 1 repository under ravdess-dataset topic.
Understanding emotions from audio files using neural networks and multiple datasets.
Speech Emotion Classification with novel Parallel CNN-Transformer model built with PyTorch, plus thorough explanations of CNNs, Transformers, and everything in between
This repository contains PyTorch implementation of 4 different models for classification of emotions of the speech.
Dynamic and static models for real-time facial emotion recognition
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
An implementation of Speech Emotion Recognition, based on HuBERT model, training with PyTorch and HuggingFace framework, and fine-tuning on the RAVDESS dataset.
In this project we use RAVDESS Dataset to classify Speech Emotion using Multi Layer Perceptron Classifier
In this work is proposed a speech emotion recognition model based on the extraction of four different features got from RAVDESS sound files and stacking the resulting matrices in a one-dimensional array by taking the mean values along the time axis. Then this array is fed into a 1-D CNN model as input.
Implementation of various models to address the speech emotion recognition (SER) task, using python and pytorch.
Speech Emotion Recognition based on RAVDESS dataset, - Summer 2021, Brain and Cognitive Science.
This repository is an import of the original repository that contains some of the models we had tested on the RAVDESS and TESS dataset for our research on Speech Emotion Recognition Models.
This project focuses on real-time Speech Emotion Recognition (SER) using the "ravdess-emotional-speech-audio" dataset. Leveraging essential libraries and Long Short-Term Memory (LSTM) networks, it processes diverse emotional states expressed in 1440 audio files. Professional actors ensure controlled representation, with 24 actors contributing
A convolutional neural network trained to classify emotions in singing voices.
Web app to detect emotion from speech using a 67% accuracy model built with 2D ConvNets trained on RAVDESS & SAVEE datasets
The SER model is capable of detecting eight different male/female emotions from audio speeches using MLP and RAVDESS model
Emotion Recognition using Speech with the help of Librosa library, MLPClassifier and RAVDESS Database.
Detected different emotions from live audio sample and model is trained on the RAVDESS dataset.
Emotion and Voice Detection using Machine Learning Python Project. This Project about to detect human Voice and Facial emotion
Translation of speech to image directly without text is an interesting and useful topic due to the potential application in computer-aided design, human to computer interaction, creation of an art form, etc. So we have focused on developing Deep learning and GANs based model which will take speech as an input from the user, analyze the emotions associated with it and accordingly generate the artwork which has been demanded by the user which will in turn provide a personalized experience. The approach used here is convolutional VQGAN to learn a codebook of context-rich visual parts, whose composition is subsequently modeled with autoregressive transformer architecture. Concept of CLIP-Contrastive Language Image-Pre-Training, also uses transformers which is a model trained to determine which caption from a set of captions best fits with a given image is used in our project. The input speech is classified into 8 different emotions using MLP classifier trained of RAVDESS emotional speech audio dataset and this acts as a base filter for the VQGAN model. Text converted from speech plays an important role in producing the final output image using CLIP model. VQGAN+CLIP model together utilizes both emotions and text to generate a more personalized artwork.
Speech Emotion Recognition project by using Multi-Layer Perceptron Model with several customized attributes for optimal performance.
emotion recognition using the ravdess dataset with CNN and Time series
This project implements a Speech Emotion Recognition (SER) model using TensorFlow Lite, specifically designed for deployment on microcontrollers like the Arduino Nano BLE33. The model is trained on the RAVDESS dataset and can recognize seven emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.
Emotion Recognition from Audio (ERA) is an innovative project that classifies human emotions from speech using advanced machine learning techniques.
Final project for the master's degree in Computer Science course "Multimodal Interaction" at the University of Rome "La Sapienza" (A.Y. 2023-2024).
Audio-image classification of emotions
My team's Machine Learning final group project about emotion classification web app to help newbie actors to act based on given scripts and emotions