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.