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.

A convolutional neural network trained to classify emotions in singing voices.

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

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.

his is a Speech Emotion Recognition system that classifies emotions from speech samples using deep learning models. The project uses four datasets: CREMAD, RAVDESS, SAVEE, and TESS. The model achieves an accuracy of 96% by combining CNN, LSTM, and CLSTM architectures, along with data augmentation techniques and feature extraction methods.

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.

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

An emotion recognition project for audio files, trained on the RAVDESS dataset, complete with a streamlit app

Deep learning system for emotion recognition from speech, achieving 50.5% accuracy on 8-class classification using transformer architecture and real-time analysis

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

A machine learning pipeline for stress detection from speech using acoustic feature extraction and classical classification models.

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

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

Speech Emotion Recognition system using CNN architecture to classify 8 emotions from audio recordings (RAVDESS dataset). Features depthwise separable convolutions for efficient audio analysis, reaching ~70% accuracy.

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

un système capable de détecter 8 émotions à partir de la voix en temps réel

emotion recognition using the ravdess dataset with CNN and Time series

Audio-image classification of emotions

This app is designed to analyze stress, depression, and emotions based on a user's voice features and responses. It uses speech analysis and machine learning to detect emotional states and provide graphs .

A deep learning project to recognize emotions from speech using a CNN and the RAVDESS dataset.

This project is about Speech Emotion Recognition using machine learning models

Developed a Natural Language Processing model for speech emotion recognition.

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