Audio-image classification of emotions
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Updated
May 18, 2023
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 …
Speech Emotion Recognition project by using Multi-Layer Perceptron Model with several customized attributes for optimal performance.
Emotion Recognition from Audio (ERA) is an innovative project that classifies human emotions from speech using advanced machine learning techniques.
emotion recognition using the ravdess dataset with CNN and Time series
Emotion and Voice Detection using Machine Learning Python Project. This Project about to detect human Voice and Facial emotion
Detected different emotions from live audio sample and model is trained on the RAVDESS dataset.
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.
The SER model is capable of detecting eight different male/female emotions from audio speeches using MLP and RAVDESS model
Web app to detect emotion from speech using a 67% accuracy model built with 2D ConvNets trained on RAVDESS & SAVEE datasets
A convolutional neural network trained to classify emotions in singing voices.
Emotion Recognition using Speech with the help of Librosa library, MLPClassifier and RAVDESS Database.
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
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.
Speech Emotion Recognition based on RAVDESS dataset, - Summer 2021, Brain and Cognitive Science.
Implementation of various models to address the speech emotion recognition (SER) task, using python and pytorch.
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.
In this project we use RAVDESS Dataset to classify Speech Emotion using Multi Layer Perceptron Classifier
An implementation of Speech Emotion Recognition, based on HuBERT model, training with PyTorch and HuggingFace framework, and fine-tuning on the RAVDESS dataset.
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