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

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.

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

Speech Emotion Classification with novel Parallel CNN-Transformer model built with PyTorch, plus thorough explanations of CNNs, Transformers, and everything in between

emotion recognition using the ravdess dataset with CNN and Time series

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

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

Emotion and Voice Detection using Machine Learning Python Project. This Project about to detect human Voice and Facial emotion

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.

Emotion Recognition using Speech with the help of Librosa library, MLPClassifier and RAVDESS Database.

An implementation of Speech Emotion Recognition, based on HuBERT model, training with PyTorch and HuggingFace framework, and fine-tuning on the RAVDESS dataset.

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

This repository contains PyTorch implementation of 4 different models for classification of emotions of the speech.

Audio-image classification of emotions

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

Understanding emotions from audio files using neural networks and multiple datasets.

Detected different emotions from live audio sample and model is trained on the RAVDESS dataset.

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

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

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