Voice Gender Recognition – IT and Computer Engineering Guide
1. Project Overview
Objective: Develop a machine learning model to classify the
gender of a speaker based on voice data using Mel-Frequency Cepstral
Coefficients (MFCC) and classifiers like SVM or Random Forest.
Scope: Enhance understanding of audio signal processing and classification
techniques.
2. Prerequisites
Knowledge: Basics of audio processing, feature extraction,
machine learning classifiers, and evaluation metrics.
Tools: Python, Librosa for audio feature extraction, Scikit-learn, NumPy,
Pandas, and Matplotlib.
Data: A dataset containing labeled voice recordings (e.g., male or female).
3. Project Workflow
- Data Collection: Obtain a labeled dataset of voice recordings.
- Feature Extraction: Extract MFCC features from the audio recordings.
- Data Preprocessing: Normalize features and prepare the data for training.
- Model Training: Train machine learning classifiers (e.g., SVM, Random Forest) on the extracted features.
- Evaluation: Evaluate model performance using metrics like accuracy and F1-score.
- Deployment: Build a system to classify gender in real-time from voice input.
4. Technical Implementation
Step 1: Import Libraries
import numpy as np
import pandas as pd
import librosa
import librosa.display
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score
Step 2: Load and Preprocess Data
# Example: Load dataset and preprocess audio files
data = [] # List to store features
labels = [] # List to store
corresponding labels
for audio_path, label in dataset:
y, sr = librosa.load(audio_path,
sr=None) # Load audio
mfcc = librosa.feature.mfcc(y=y,
sr=sr, n_mfcc=13) # Extract MFCC
features
mfcc_mean = np.mean(mfcc,
axis=1) # Compute mean of MFCC
coefficients
data.append(mfcc_mean)
labels.append(label)
# Convert to NumPy arrays
X = np.array(data)
y = np.array(labels)
Step 3: Train-Test Split
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
Step 4: Train the Model
# Train a Support Vector Machine (SVM) classifier
model = SVC(kernel='linear', random_state=42)
model.fit(X_train, y_train)
Step 5: Evaluate the Model
# Make predictions
y_pred = model.predict(X_test)
# Evaluate performance
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Classification Report:\n', classification_report(y_test, y_pred))
5. Results and Insights
Analyze the model's accuracy and error metrics to determine the reliability of gender classification based on voice data. Understand feature contributions and identify any biases in the dataset.
6. Challenges and Mitigation
Noise in Data: Apply noise reduction techniques or use
robust feature extraction methods.
Imbalanced Dataset: Address class imbalance using techniques like SMOTE or
weighted loss functions.
7. Future Enhancements
Extend the model to classify additional attributes like age
group or emotion.
Incorporate deep learning techniques like recurrent neural networks (RNNs) for
sequential data analysis.
8. Conclusion
The Voice Gender Recognition project demonstrates the application of audio signal processing and machine learning in creating practical classification systems.