Music Genre Classification

 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.