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

💻 🤖 A summary on our attempts at using Deep Learning approaches for Emotional Text to Speech 🔈

[ICASSP 2023] Official Tensorflow implementation of "Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition".

[RAVDESS] Speech Emotion Recognition with Convolutional Attention based Bi-GRU. (Best test accuracy of 87%)

Emotion is an intuitive feeling which can be determined from any person’s circumstances and surroundings. But in this project, we tried to identify the emotional state of a person using his voice as input.

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

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.

CNN-LSTM based SER model using RAVDESS database

Offical implementation of paper "MSAF: Multimodal Split Attention Fusion"

This repository contains the source code for my final year project for my undergraduate degree in MTU.

Collaborated to create a Machine Learning model trained and tested with a Random Forest model to predict primary emotion based on input audio file. Data cleaned and trained in a Jupyter Notebook using Pandas and Librosa. Results visualized using Pandas, Tableau, and JavaScript functions with bootstrap in a dynamic HTML website.

Pytorch speech emotion recognition for RAVDESS dataset with CNN.

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

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.

Use GANs to generate spectrogram of speech. The generated spectrogram will be conditioned on emotion

Classifying Audio to Emotion

1,440 audio files (.wav), i.e. speech files, from 24 actors that are categorized into 8 separate emotions.