File size: 1,611 Bytes
961ca22 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
# SentimentSound
## Overview
This is a deep learning model for Speech Emotion Recognition that can classify audio clips into different emotional states. The model is trained on a dataset of speech samples and can identify emotions such as neutral, calm, happy, sad, angry, fearful, disgust, and surprised.
## Model Details
- **Model Type:** Hybrid Neural Network (CNN + LSTM)
- **Input:** Audio features extracted from 3-second wav files
- **Output:** Emotion classification
### Supported Emotions
- Neutral
- Calm
- Happy
- Sad
- Angry
- Fearful
- Disgust
- Surprised
## Installation
### Clone the Repository
```bash
git clone https://github.com/Vishal-Padia/SentimentSound.git
```
### Dependencies
```bash
pip install -r requirements.txt
```
### Usage Example
```bash
python emotion_predictor.py
```
## Model Performance
- **Accuracy:** 85%
- **Evaluation Metrics:** Confusion matrix below
## Training Details
- **Feature Extraction:**
- MFCC
- Spectral Centroid
- Chroma Features
- Spectral Contrast
- Zero Crossing Rate
- Spectral Rolloff
- **Augmentation:** Random noise and scaling applied
- **Training Techniques:**
- Class weighted loss
- AdamW optimizer
- Learning rate scheduling
- Gradient clipping
## Limitations
- Works best with clear speech recordings
- Optimized for 3-second audio clips
- Performance may vary with different audio sources
## Acknowledgments
- Dataset used for training (https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio) |