Emotion Detection from Voice

 Emotion Detection from Voice – IT and Computer Engineering Guide

1. Project Overview

Objective: Develop a system to detect human emotions from voice recordings using machine learning and audio signal processing techniques.
Scope: Enable applications in customer service, virtual assistants, and mental health monitoring.

2. Prerequisites

Knowledge: Familiarity with Python, machine learning, and basic audio signal processing.
Tools: Python, Librosa, Scikit-learn, TensorFlow/Keras, and NumPy.
Dataset: Publicly available datasets such as the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) or custom audio data.

3. Project Workflow

- Dataset Collection: Gather labeled audio files representing various emotions.

- Feature Extraction: Extract audio features like Mel-Frequency Cepstral Coefficients (MFCCs), chroma, and spectral contrast.

- Data Preprocessing: Normalize and split the dataset for training and testing.

- Model Development: Train a machine learning or deep learning model to classify emotions.

- Evaluation: Test the model and refine it based on performance metrics.

4. Technical Implementation

Step 1: Import Libraries


import librosa
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout

Step 2: Feature Extraction


def extract_features(file_path):
    audio, sample_rate = librosa.load(file_path, res_type='kaiser_fast')
    mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)
    return np.mean(mfccs.T, axis=0)

# Collect features and labels
features, labels = [], []
for file_path, emotion in dataset:  # Replace 'dataset' with your dataset structure
    features.append(extract_features(file_path))
    labels.append(emotion)
features = np.array(features)
labels = np.array(labels)

Step 3: Preprocess Data


# Encode labels
label_encoder = LabelEncoder()
labels = label_encoder.fit_transform(labels)

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

Step 4: Train the Model


# Build model
model = Sequential([
    Dense(256, activation='relu', input_shape=(features.shape[1],)),
    Dropout(0.3),
    Dense(128, activation='relu'),
    Dropout(0.3),
    Dense(len(label_encoder.classes_), activation='softmax')
])

# Compile model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train model
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

Step 5: Evaluate and Save the Model


# Evaluate model
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test Accuracy: {accuracy}")

# Save model
model.save('emotion_detection_model.h5')

5. Results and Insights

Analyze the model's accuracy and misclassification patterns. Document insights into how feature extraction and model design impacted performance.

6. Challenges and Mitigation

Noise in Audio: Apply preprocessing techniques like noise reduction.
Emotion Overlap: Use a diverse dataset and advanced features to improve distinction.

7. Future Enhancements

Incorporate deep learning architectures like recurrent neural networks (RNNs) for temporal analysis.
Extend to multi-modal emotion detection using audio-visual data.

8. Conclusion

The Emotion Detection from Voice project demonstrates the potential of combining audio signal processing and machine learning to interpret human emotions for real-world applications.