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.

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.

Dynamic and static models for real-time facial emotion recognition

In this project we use RAVDESS Dataset to classify Speech Emotion using Multi Layer Perceptron Classifier

Speech Emotion Recognition project by using Multi-Layer Perceptron Model with several customized attributes for optimal performance.

The SER model is capable of detecting eight different male/female emotions from audio speeches using MLP and RAVDESS model

Implementation of various models to address the speech emotion recognition (SER) task, using python and pytorch.

Audio-image classification of emotions

This project is about Speech Emotion Recognition using machine learning models

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 …

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.

Emotion Recognition from Audio (ERA) is an innovative project that classifies human emotions from speech using advanced machine learning techniques.

Speech Emotion Recognition based on RAVDESS dataset, - Summer 2021, Brain and Cognitive Science.

Web app to detect emotion from speech using a 67% accuracy model built with 2D ConvNets trained on RAVDESS & SAVEE datasets

emotion recognition using the ravdess dataset with CNN and Time series

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.