Music Genre Classification – IT and Computer Engineering Guide
1. Project Overview
Objective: Classify music tracks into genres using audio
features and machine learning techniques.
Scope: Develop a machine learning model leveraging audio feature extraction to
identify genres accurately.
2. Prerequisites
Knowledge: Basics of Python programming, audio processing,
and machine learning.
Tools: Python, Librosa, Scikit-learn, Pandas, NumPy, Matplotlib, and Seaborn.
Dataset: GTZAN dataset or any collection of labeled music tracks.
3. Project Workflow
- Dataset Preparation: Download or create a dataset with labeled music tracks.
- Feature Extraction: Use libraries like Librosa to extract features such as Mel-Frequency Cepstral Coefficients (MFCCs), spectral contrast, and chroma features.
- Data Preprocessing: Normalize and prepare the feature set for training.
- Model Development: Train classification models like Random Forest, SVM, or Neural Networks.
- Model Evaluation: Evaluate using metrics like accuracy, precision, recall, and confusion matrix.
- Visualization: Visualize feature importance and classification results.
- Deployment: Create a tool or app to classify music in real-time or batch mode.
4. Technical Implementation
Step 1: Import Libraries
import librosa
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
Step 2: Load and Extract Features
# Load audio file and extract features
def extract_features(file_name):
audio, sample_rate =
librosa.load(file_name, res_type='kaiser_fast')
mfccs = librosa.feature.mfcc(y=audio,
sr=sample_rate, n_mfcc=40)
mfccs_mean = np.mean(mfccs.T, axis=0)
return mfccs_mean
# Example usage
features = []
labels = []
for file in audio_files: # Replace with
actual file list
features.append(extract_features(file))
labels.append(get_label(file)) # Replace with actual label extraction
# Convert to DataFrame
features_df = pd.DataFrame(features)
labels_df = pd.Series(labels)
Step 3: Split the Dataset
X_train, X_test, y_train, y_test = train_test_split(features_df, labels_df,
test_size=0.2, random_state=42)
Step 4: Train the Model
# Train a Random Forest Classifier
rf_model = RandomForestClassifier(random_state=42)
rf_model.fit(X_train, y_train)
Step 5: Evaluate the Model
# Evaluate the model
y_pred = rf_model.predict(X_test)
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
5. Results and Insights
Analyze classification performance and identify common misclassifications to refine the model.
6. Challenges and Mitigation
Audio Variability: Ensure robust feature extraction to
handle diverse audio samples.
Class Imbalance: Use data augmentation or oversampling techniques to address
imbalance.
7. Future Enhancements
Experiment with deep learning techniques like CNNs or RNNs
for end-to-end audio classification.
Incorporate additional metadata such as lyrics or artist information.
8. Conclusion
The Music Genre Classification project highlights the application of machine learning in audio analysis, paving the way for intelligent music categorization and recommendation systems.